rm(list=ls()) # clear all
library(knitr)
opts_chunk$set(comment="",cache=FALSE,message=FALSE,warning=FALSE,echo=TRUE)
# load all required packages
library(caret)
library(ggplot2)
library(digest)
library(plyr)
library(gridExtra)
library(gmodels)
library(chron)
library(lubridate)
library(date)
library(dplyr)
library(data.table)
library(dplyr)
library(dtplyr)
library(tidyr)
library(knitr)
library(lme4)
library(sjmisc)
library(sjPlot)
library(lmerTest)
library(gridExtra)
library(reshape2)
library(mgcv)
library(itsadug)
library(zoo)
library(geepack)
library(RColorBrewer)
library(ggplot2)
library(scales)
library(gdata)
library(randomForest)
# metaanalysis
library(meta)
dir<-"C:/Users/nth2111/My files/Dr Kuhn/Microbiome/Rprac/analysis/metamicrobiome_breastfeeding/" #to be replaced by your directory
source(paste(dir,"miscfun.microbiome.R",sep=""))
# load data
load(paste(dir,"data/sam.rm.rda",sep=""))
load(paste(dir,"data/taxlist.filter.rda",sep=""))
load(paste(dir,"data/sumstud.rda",sep=""))
load(paste(dir,"data/bangladesh.rda",sep=""))
All studies
kable(n6.bf.all)
| ExclusiveBF | Non_exclusiveBF | No_BF | sum | |
|---|---|---|---|---|
| Bangladesh | 138 | 178 | 6 | 322 |
| Canada | 86 | 48 | 33 | 167 |
| Haiti | 37 | 11 | 0 | 48 |
| South Africa | 86 | 57 | 0 | 143 |
| USA(CA_FL) | 150 | 68 | 12 | 230 |
| USA(CA_MA_MO) | 38 | 66 | 116 | 220 |
| USA(NC) | 12 | 8 | 1 | 21 |
| All studies | 547 | 436 | 168 | 1151 |
All studies for stratified meta-analysis by birth mode
kable(n6.bm.all)
| Vaginal | C-section | sum | |
|---|---|---|---|
| Canada | 130 | 37 | 167 |
| Haiti | 42 | 6 | 48 |
| USA(CA_FL) | 162 | 65 | 227 |
| USA(CA_MA_MO) | 150 | 78 | 228 |
| All studies | 484 | 186 | 670 |
With Generalized additive mixed model (GAMM) fit and 95%CI.
rmdat.ha$personid<-as.factor(rmdat.ha$sampleid)
rmdat.hav$personid<-as.factor(rmdat.hav$sampleid)
rmdat.ca$personid<-as.factor(rmdat.ca$sampleid)
rmdat.all<-rbind.fill(rmdat.ha,rmdat.rm,rmdat.usbmk,rmdat.uw,rmdat.unc,rmdat.hav,rmdat.ca)
rmdat.all$study<-as.factor(rmdat.all$study)
b2<-gamm(age.predicted~s(age.sample,by=study),family=gaussian,
data=rmdat.all,random=list(personid=~1))
pred <- predict(b2$gam, newdata = rmdat.all,se.fit=TRUE)
datfit<-cbind(rmdat.all, fit=pred$fit,ul=(pred$fit+(1.96*pred$se.fit)),ll=(pred$fit-(1.96*pred$se.fit)))
rmdat.all.6<-rmdat.all[rmdat.all$age.sample<=6,]
rmdat.all.6<-rmdat.all[rmdat.all$age.sample<=6,]
datfit.6<-datfit[datfit$age.sample<=6,]
p<-ggplot()+ geom_point(data = rmdat.all.6, aes(x = age.sample, y = age.predicted, group = personid, colour=bf))+
geom_line(data = rmdat.all.6, aes(x = age.sample, y = age.predicted, group = personid, colour=bf),size=0.3)+
geom_line(data = datfit.6,aes(x = age.sample, y = fit),size = 1)+
#geom_line(data = datfit.6,aes(x = age.sample, y = ul),size = 0.1)+
#geom_line(data = datfit.6,aes(x = age.sample, y = ll),size = 0.1)+
geom_ribbon(data = datfit.6,aes(x=age.sample, ymax=ul, ymin=ll), alpha=.5)+
xlab("Chronological age (month)") +ylab("Microbiome age (month)")+
labs(color='')+
theme(legend.position = "bottom",
#plot.background = element_blank(),
#panel.background = element_blank()
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
strip.background =element_rect(fill="white"))+
facet_grid(.~ pop)
p
rmdat.all.nona<-rmdat.all[!is.na(rmdat.all$bf),]
rmdat.all.nona$pop.bf<-paste(rmdat.all.nona$pop,rmdat.all.nona$bf,sep="_")
rmdat.all.nona$pop.bf<-as.factor(rmdat.all.nona$pop.bf)
b2<-gamm(age.predicteds~s(age.sample,by=pop.bf),family=gaussian,
data=rmdat.all.nona,random=list(personid=~1))
pred <- predict(b2$gam, newdata = rmdat.all.nona,se.fit=TRUE)
datfit<-cbind(rmdat.all.nona, fit=pred$fit,ul=(pred$fit+(1.96*pred$se.fit)),ll=(pred$fit-(1.96*pred$se.fit)))
p.rms.bf<-ggplot()+ geom_point(data = subset(rmdat.all.nona,age.sample<=6), aes(x = age.sample, y = age.predicteds, group = personid, colour=bf),size=1)+
geom_line(data = subset(rmdat.all.nona,age.sample<=6), aes(x = age.sample, y = age.predicteds, group = personid, colour=bf),size=0.1)+
geom_line(data = subset(datfit,age.sample<=6),aes(x = age.sample, y = fit, colour=bf),size = 1)+
geom_ribbon(data = subset(datfit,age.sample<=6),aes(x=age.sample, ymax=ul, ymin=ll, fill=bf), alpha=.4)+guides(fill=FALSE)+
xlab("Chronological age (month)") +ylab("Standardized microbiome age")+
labs(color='')+
theme(legend.position = "bottom",
#plot.background = element_blank(),
#panel.background = element_blank()
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
strip.background =element_rect(fill="white"))+
facet_grid(.~ pop)
p.rms.bf
Meta-analysis models based on adjusted estimate (adjusted for age of infant at sample collection) and standard error from linear mixed effect models.
rmshare.sum.6<-as.data.frame(rmshare.sum.6s)
rm.nebf<-metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=rmshare.sum.6,sm="RD", backtransf=FALSE)
forest(rm.nebf,smlab="Standardized \n microbiome age difference",sortvar=rmshare.sum.6$pop)
rm.nebf
RD 95%-CI %W(fixed)
Thompson et al 2015 ( USA(NC) ) 0.8754 [ 0.1366; 1.6142] 1.5
Wood et al 2017 ( South Africa ) 0.1214 [-0.2963; 0.5390] 4.7
Pannaraj et al 2017 ( USA(CA_FL) ) 0.2665 [ 0.0464; 0.4866] 17.1
Bender et al 2016 ( Haiti ) -0.1769 [-0.8203; 0.4664] 2.0
Subramanian et al 2014 ( Bangladesh ) 0.0568 [-0.0605; 0.1741] 60.2
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.7355 [ 0.3600; 1.1109] 5.9
Azad et al 2015 ( Canada ) 0.6149 [ 0.3048; 0.9250] 8.6
%W(random)
Thompson et al 2015 ( USA(NC) ) 7.3
Wood et al 2017 ( South Africa ) 13.4
Pannaraj et al 2017 ( USA(CA_FL) ) 18.7
Bender et al 2016 ( Haiti ) 8.7
Subramanian et al 2014 ( Bangladesh ) 21.0
Sordillo et al 2017 ( USA(CA_MA_MO) ) 14.5
Azad et al 2015 ( Canada ) 16.3
Number of studies combined: k = 7
RD 95%-CI z p-value
Fixed effect model 0.1913 [0.1003; 0.2823] 4.12 < 0.0001
Random effects model 0.3336 [0.0894; 0.5778] 2.68 0.0074
Quantifying heterogeneity:
tau^2 = 0.0702; H = 2.06 [1.42; 2.98]; I^2 = 76.4% [50.4%; 88.8%]
Test of heterogeneity:
Q d.f. p-value
25.40 6 0.0003
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=rm.nebf$studlab,pval=rm.nebf$pval))
| study | pval |
|---|---|
| Thompson et al 2015 ( USA(NC) ) | 0.0202100021212404 |
| Wood et al 2017 ( South Africa ) | 0.569010986228987 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.0176451947099079 |
| Bender et al 2016 ( Haiti ) | 0.589859305402284 |
| Subramanian et al 2014 ( Bangladesh ) | 0.342537683608756 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 0.000123424504683378 |
| Azad et al 2015 ( Canada ) | 0.000101690866421991 |
rmshare.sum.6.noha<-rmshare.sum.6[!rownames(rmshare.sum.6) %in% "ha",]
rm.nebf<-metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=rmshare.sum.6.noha,sm="RD", backtransf=FALSE)
forest(rm.nebf,smlab="Standardized \n microbiome age difference",sortvar=rmshare.sum.6.noha$pop)
rm.nebf
RD 95%-CI %W(fixed)
Thompson et al 2015 ( USA(NC) ) 0.8754 [ 0.1366; 1.6142] 1.5
Wood et al 2017 ( South Africa ) 0.1214 [-0.2963; 0.5390] 4.8
Pannaraj et al 2017 ( USA(CA_FL) ) 0.2665 [ 0.0464; 0.4866] 17.4
Subramanian et al 2014 ( Bangladesh ) 0.0568 [-0.0605; 0.1741] 61.4
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.7355 [ 0.3600; 1.1109] 6.0
Azad et al 2015 ( Canada ) 0.6149 [ 0.3048; 0.9250] 8.8
%W(random)
Thompson et al 2015 ( USA(NC) ) 8.1
Wood et al 2017 ( South Africa ) 14.8
Pannaraj et al 2017 ( USA(CA_FL) ) 20.4
Subramanian et al 2014 ( Bangladesh ) 22.9
Sordillo et al 2017 ( USA(CA_MA_MO) ) 15.9
Azad et al 2015 ( Canada ) 17.8
Number of studies combined: k = 6
RD 95%-CI z p-value
Fixed effect model 0.1988 [0.1069; 0.2908] 4.24 < 0.0001
Random effects model 0.3835 [0.1247; 0.6422] 2.90 0.0037
Quantifying heterogeneity:
tau^2 = 0.0726; H = 2.20 [1.49; 3.25]; I^2 = 79.3% [54.7%; 90.5%]
Test of heterogeneity:
Q d.f. p-value
24.11 5 0.0002
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=rm.nebf$studlab,pval=rm.nebf$pval))
| study | pval |
|---|---|
| Thompson et al 2015 ( USA(NC) ) | 0.0202100021212404 |
| Wood et al 2017 ( South Africa ) | 0.569010986228987 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.0176451947099079 |
| Subramanian et al 2014 ( Bangladesh ) | 0.342537683608756 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 0.000123424504683378 |
| Azad et al 2015 ( Canada ) | 0.000101690866421991 |
rmshare.sum.6.nounc<-rmshare.sum.6[!rownames(rmshare.sum.6) %in% "unc",]
rm.nebf<-metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=rmshare.sum.6.nounc,sm="RD", backtransf=FALSE)
forest(rm.nebf,smlab="Standardized \n microbiome age difference",sortvar=rmshare.sum.6.nounc$pop)
rm.nebf
RD 95%-CI %W(fixed)
Wood et al 2017 ( South Africa ) 0.1214 [-0.2963; 0.5390] 4.8
Pannaraj et al 2017 ( USA(CA_FL) ) 0.2665 [ 0.0464; 0.4866] 17.4
Bender et al 2016 ( Haiti ) -0.1769 [-0.8203; 0.4664] 2.0
Subramanian et al 2014 ( Bangladesh ) 0.0568 [-0.0605; 0.1741] 61.1
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.7355 [ 0.3600; 1.1109] 6.0
Azad et al 2015 ( Canada ) 0.6149 [ 0.3048; 0.9250] 8.7
%W(random)
Wood et al 2017 ( South Africa ) 14.3
Pannaraj et al 2017 ( USA(CA_FL) ) 20.4
Bender et al 2016 ( Haiti ) 9.1
Subramanian et al 2014 ( Bangladesh ) 23.1
Sordillo et al 2017 ( USA(CA_MA_MO) ) 15.5
Azad et al 2015 ( Canada ) 17.6
Number of studies combined: k = 6
RD 95%-CI z p-value
Fixed effect model 0.1808 [0.0891; 0.2725] 3.87 0.0001
Random effects model 0.2909 [0.0458; 0.5360] 2.33 0.0200
Quantifying heterogeneity:
tau^2 = 0.0640; H = 2.10 [1.41; 3.13]; I^2 = 77.3% [49.6%; 89.8%]
Test of heterogeneity:
Q d.f. p-value
22.05 5 0.0005
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=rm.nebf$studlab,pval=rm.nebf$pval))
| study | pval |
|---|---|
| Wood et al 2017 ( South Africa ) | 0.569010986228987 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.0176451947099079 |
| Bender et al 2016 ( Haiti ) | 0.589859305402284 |
| Subramanian et al 2014 ( Bangladesh ) | 0.342537683608756 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 0.000123424504683378 |
| Azad et al 2015 ( Canada ) | 0.000101690866421991 |
rmshare.sum.6.nohav<-rmshare.sum.6[!rownames(rmshare.sum.6) %in% "hav",]
rm.nebf<-metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=rmshare.sum.6.nohav,sm="RD", backtransf=FALSE)
forest(rm.nebf,smlab="Standardized \n microbiome age difference",sortvar=rmshare.sum.6.nohav$pop)
rm.nebf
RD 95%-CI %W(fixed)
Thompson et al 2015 ( USA(NC) ) 0.8754 [ 0.1366; 1.6142] 1.6
Wood et al 2017 ( South Africa ) 0.1214 [-0.2963; 0.5390] 5.0
Pannaraj et al 2017 ( USA(CA_FL) ) 0.2665 [ 0.0464; 0.4866] 18.2
Bender et al 2016 ( Haiti ) -0.1769 [-0.8203; 0.4664] 2.1
Subramanian et al 2014 ( Bangladesh ) 0.0568 [-0.0605; 0.1741] 63.9
Azad et al 2015 ( Canada ) 0.6149 [ 0.3048; 0.9250] 9.1
%W(random)
Thompson et al 2015 ( USA(NC) ) 7.4
Wood et al 2017 ( South Africa ) 15.0
Pannaraj et al 2017 ( USA(CA_FL) ) 22.8
Bender et al 2016 ( Haiti ) 9.0
Subramanian et al 2014 ( Bangladesh ) 26.7
Azad et al 2015 ( Canada ) 19.0
Number of studies combined: k = 6
RD 95%-CI z p-value
Fixed effect model 0.1574 [0.0636; 0.2512] 3.29 0.0010
Random effects model 0.2602 [0.0267; 0.4936] 2.18 0.0290
Quantifying heterogeneity:
tau^2 = 0.0495; H = 1.83 [1.20; 2.80]; I^2 = 70.3% [30.5%; 87.3%]
Test of heterogeneity:
Q d.f. p-value
16.83 5 0.0048
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=rm.nebf$studlab,pval=rm.nebf$pval))
| study | pval |
|---|---|
| Thompson et al 2015 ( USA(NC) ) | 0.0202100021212404 |
| Wood et al 2017 ( South Africa ) | 0.569010986228987 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.0176451947099079 |
| Bender et al 2016 ( Haiti ) | 0.589859305402284 |
| Subramanian et al 2014 ( Bangladesh ) | 0.342537683608756 |
| Azad et al 2015 ( Canada ) | 0.000101690866421991 |
load(paste(dir,"data/rmshare.vagcs.rda",sep=""))
rm.nebf<-metagen(estimate.nebf, se.nebf, studlab=study,data=rmshare.vag,sm="RD", backtransf=FALSE)
forest(rm.nebf,smlab="Microbiome age difference")
rm.nebf
RD 95%-CI %W(fixed)
Azad et al 2015 (Canada) 2.0223 [ 1.0746; 2.9701] 23.9
Bender et al 2016 (Haiti) -0.5119 [-2.7851; 1.7614] 4.1
Pannaraj et al 2017 (USA(CA_FL)) 0.5934 [-0.0958; 1.2826] 45.1
Sordillo et al 2017 (USA(CA_MA_MO)) 1.5749 [ 0.6821; 2.4677] 26.9
%W(random)
Azad et al 2015 (Canada) 27.7
Bender et al 2016 (Haiti) 10.8
Pannaraj et al 2017 (USA(CA_FL)) 32.8
Sordillo et al 2017 (USA(CA_MA_MO)) 28.8
Number of studies combined: k = 4
RD 95%-CI z p-value
Fixed effect model 1.1523 [0.6894; 1.6152] 4.88 < 0.0001
Random effects model 1.1519 [0.2829; 2.0209] 2.60 0.0094
Quantifying heterogeneity:
tau^2 = 0.4763; H = 1.70 [1.00; 2.92]; I^2 = 65.4% [0.0%; 88.2%]
Test of heterogeneity:
Q d.f. p-value
8.68 3 0.0338
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=rm.nebf$studlab,pval=rm.nebf$pval))
| study | pval |
|---|---|
| Azad et al 2015 (Canada) | 2.88635033937751e-05 |
| Bender et al 2016 (Haiti) | 0.658981004403214 |
| Pannaraj et al 2017 (USA(CA_FL)) | 0.0914811823311381 |
| Sordillo et al 2017 (USA(CA_MA_MO)) | 0.000545678208685348 |
rm.nebf<-metagen(estimate.nebf, se.nebf, studlab=study,data=rmshare.cs,sm="RD", backtransf=FALSE)
forest(rm.nebf,smlab="Microbiome age difference")
rm.nebf
RD 95%-CI %W(fixed)
Azad et al 2015 (Canada) 0.6820 [-1.3391; 2.7031] 14.4
Bender et al 2016 (Haiti) -0.3508 [-4.3974; 3.6958] 3.6
Pannaraj et al 2017 (USA(CA_FL)) 0.7880 [-0.2996; 1.8755] 49.7
Sordillo et al 2017 (USA(CA_MA_MO)) 1.1977 [-0.1522; 2.5476] 32.3
%W(random)
Azad et al 2015 (Canada) 14.4
Bender et al 2016 (Haiti) 3.6
Pannaraj et al 2017 (USA(CA_FL)) 49.7
Sordillo et al 2017 (USA(CA_MA_MO)) 32.3
Number of studies combined: k = 4
RD 95%-CI z p-value
Fixed effect model 0.8641 [0.0971; 1.6310] 2.21 0.0272
Random effects model 0.8641 [0.0971; 1.6310] 2.21 0.0272
Quantifying heterogeneity:
tau^2 = 0; H = 1.00 [1.00; 1.17]; I^2 = 0.0% [0.0%; 27.2%]
Test of heterogeneity:
Q d.f. p-value
0.63 3 0.8893
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=rm.nebf$studlab,pval=rm.nebf$pval))
| study | pval |
|---|---|
| Azad et al 2015 (Canada) | 0.508396867999409 |
| Bender et al 2016 (Haiti) | 0.865079808120753 |
| Pannaraj et al 2017 (USA(CA_FL)) | 0.155598040795797 |
| Sordillo et al 2017 (USA(CA_MA_MO)) | 0.0820319111581724 |
rmshare.conbf<-rmshare.conbfs
rmshare.conbf$pop<-c("Bangladesh","USA(CA_FL)","USA(CA_MA_MO)","Canada","USA(NC)")
rmshare.conbf<-rmshare.conbf[order(rmshare.conbf$pop),]
rm.conbf<-metagen(estimate.conbf, se.conbf, studlab=study,data=rmshare.conbf,sm="RD", backtransf=FALSE)
forest(rm.conbf,smlab="Standardized \n microbiome age difference",sortvar=rmshare.conbf$pop)
rm.conbf
RD 95%-CI %W(fixed)
Subramanian et al 2014 (Bangladesh) 0.0308 [-0.0757; 0.1372] 44.1
Azad et al 2015 (Canada) 0.5818 [ 0.4102; 0.7534] 17.0
Pannaraj et al 2017 (USA(CA_FL)) 0.3161 [ 0.1504; 0.4819] 18.2
Sordillo et al 2017 (USA(CA_MA_MO)) 0.5214 [ 0.3626; 0.6802] 19.8
Thompson et al 2015 (USA(NC)) 0.2911 [-0.4807; 1.0628] 0.8
%W(random)
Subramanian et al 2014 (Bangladesh) 24.0
Azad et al 2015 (Canada) 22.6
Pannaraj et al 2017 (USA(CA_FL)) 22.7
Sordillo et al 2017 (USA(CA_MA_MO)) 22.9
Thompson et al 2015 (USA(NC)) 7.8
Number of studies combined: k = 5
RD 95%-CI z p-value
Fixed effect model 0.2758 [0.2051; 0.3465] 7.64 < 0.0001
Random effects model 0.3526 [0.0926; 0.6125] 2.66 0.0079
Quantifying heterogeneity:
tau^2 = 0.0703; H = 3.24 [2.28; 4.61]; I^2 = 90.5% [80.7%; 95.3%]
Test of heterogeneity:
Q d.f. p-value
41.99 4 < 0.0001
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=rm.conbf$studlab,pval=rm.conbf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 (Bangladesh) | 0.570837554151296 |
| Azad et al 2015 (Canada) | 2.9999698377109e-11 |
| Pannaraj et al 2017 (USA(CA_FL)) | 0.000185565976276297 |
| Sordillo et al 2017 (USA(CA_MA_MO)) | 1.24192582145007e-10 |
| Thompson et al 2015 (USA(NC)) | 0.459754398037424 |
With GAMM fit and 95%CI.
rmdat.rm<-rmdat.rm %>% group_by(personid) %>% arrange(personid,age.sample) %>%
mutate(month.food6=cut(month.food, breaks=c(-Inf, 6, Inf), labels=c("<=6 months",">6 months")),
month.food5=cut(month.food, breaks=c(-Inf, 5, Inf), labels=c("<=5 months",">5 months")),
month.food4=cut(month.food, breaks=c(-Inf, 4, Inf), labels=c("<=4 months",">4 months")),
month.exbf3=cut(month.exbf, breaks=c(-Inf, 3, Inf), labels=c("<=3 months",">3 months")),
month.exbf2=cut(month.exbf, breaks=c(-Inf, 2, Inf), labels=c("<=2 months",">2 months")),
month.exbf1=cut(month.exbf, breaks=c(-Inf, 1, Inf), labels=c("<=1 months",">1 months")))
Number of infants by duration of bf in the test set
table(rmdat.rm$month.exbf2[duplicated(rmdat.rm$child.id)==FALSE])
<=2 months >2 months
26 13
Number of samples by duration of bf in the test set
table(rmdat.rm$month.exbf2)
<=2 months >2 months
483 252
Number of samples by duration of bf in the test set (>6 months only)
table(rmdat.rm$month.exbf2[rmdat.rm$age.sample>6])
<=2 months >2 months
305 176
rmdat.rm<-as.data.frame(rmdat.rm)
b2<-gamm(age.predicted~s(age.sample,by=month.exbf2) +month.exbf2,family=gaussian,
data=rmdat.rm,random=list(personid=~1))
pred <- predict(b2$gam, newdata = rmdat.rm,se.fit=TRUE)
datfit<-cbind(rmdat.rm, fit=pred$fit,ul=(pred$fit+(1.96*pred$se.fit)),ll=(pred$fit-(1.96*pred$se.fit)))
pexbf2<-ggplot()+ geom_point(data = rmdat.rm, aes(x = age.sample, y = age.predicted, group = personid, colour=month.exbf2))+
geom_line(data = rmdat.rm, aes(x = age.sample, y = age.predicted, group = personid, colour=month.exbf2),size=0.3)+
geom_line(data = datfit,aes(x = age.sample, y = fit, colour=month.exbf2),size = 1.5)+
geom_ribbon(data = datfit,aes(x=age.sample, ymax=ul, ymin=ll,group=month.exbf2), alpha=.5)+
#theme(legend.position = "bottom")+
theme(legend.title = element_text(colour="black", size=10))+
labs(color='Duration EBF')+
scale_x_continuous(breaks=seq(from=0,to=24,by=3),
labels=seq(from=0,to=24,by=3))+
#theme(legend.text = element_text(colour="black", size = 10))+
theme(legend.position = c(0.15,0.85),legend.title=element_text(size=8),legend.text=element_text(size=6))+
theme(legend.key.size = unit(0.5, "cm"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())+
xlab("Chronological age (month)") +ylab("Microbiome age (month)")
pexbf2
rmdat.rm.6plus15<-rmdat.rm[rmdat.rm$age.sample>6 & rmdat.rm$age.sample<15,]
b2<-gamm(age.predicted~s(age.sample,by=month.exbf2) +month.exbf2,family=gaussian,
data=rmdat.rm.6plus15,random=list(personid=~1))
Test for age > 6 months and <15months GAM part
kable(summary(b2$gam)$p.table)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 11.516456 | 0.4287802 | 26.858644 | 0.0000000 |
| month.exbf2>2 months | -1.634246 | 0.7401967 | -2.207854 | 0.0280084 |
kable(summary(b2$gam)$s.table)
| edf | Ref.df | F | p-value | |
|---|---|---|---|---|
| s(age.sample):month.exbf2<=2 months | 1.000001 | 1.000001 | 120.79393 | 0 |
| s(age.sample):month.exbf2>2 months | 1.000000 | 1.000000 | 60.88972 | 0 |
LME part
kable(summary(b2$lme)$tTable)
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| X(Intercept) | 11.516456 | 0.4302022 | 264 | 26.769865 | 0.0000000 |
| Xmonth.exbf2>2 months | -1.634246 | 0.7426508 | 37 | -2.200557 | 0.0340874 |
| Xs(age.sample):month.exbf2<=2 monthsFx1 | 2.352653 | 0.2147700 | 264 | 10.954291 | 0.0000000 |
| Xs(age.sample):month.exbf2>2 monthsFx1 | 2.231823 | 0.2869630 | 264 | 7.777391 | 0.0000000 |
load(paste(dir,"data/SrfFit.rml6.shareg7.train.test.rda",sep=""))
Straining$age.predicted <- predict(SrfFit.rml6.share, newdata = Straining)
actual<-Straining$Sage
predict<-Straining$age.predicted
R2 <- 1 - (sum((actual-predict )^2)/sum((actual-mean(actual))^2))
R2<-round(R2,2)
ptrain<-ggplot() +geom_point(data=Straining,aes(x=Sage, y=age.predicted))+
theme(legend.text = element_text(colour="black", size = 10))+
annotate("text", x=15, y=5,label=paste("R squared =",R2,sep=" ")) +
labs(title="Training set")+
theme(legend.key.size = unit(0.5, "cm"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())+
xlab("Chronological age (month)") +ylab("Microbiome age (month)")
actual<-rmdat.rm$age.sample
predict<-rmdat.rm$age.predicted
R2 <- 1 - (sum((actual-predict )^2)/sum((actual-mean(actual))^2))
R2<-round(R2,2)
ptest<-ggplot() +geom_point(data=rmdat.rm,aes(x=age.sample, y=age.predicted))+
theme(legend.text = element_text(colour="black", size = 10))+
annotate("text", x=15, y=5,label=paste("R squared =",R2,sep=" ")) +
labs(title="Test set")+
theme(legend.key.size = unit(0.5, "cm"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())+
xlab("Chronological age (month)") +ylab("Microbiome age (month)")
grid.arrange(ptrain, ptest,nrow=1)
With GAMM fit and 95%CI.
#standardize
load(paste(dir,"data/alphamean7s.pooled.rda",sep=""))
load(paste(dir,"data/alphaall7s.rda",sep=""))
load(paste(dir,"data/alphameta.allindex7s.rda",sep=""))
alphap$pop<-as.factor(alphap$pop)
b2<-gamm(shannon~s(age.sample,by=pop),family=gaussian,
data=alphap,random=list(personid=~1))
pred <- predict(b2$gam, newdata = alphap,se.fit=TRUE)
datfit<-cbind(alphap, fit=pred$fit,ul=(pred$fit+(1.96*pred$se.fit)),ll=(pred$fit-(1.96*pred$se.fit)))
Samples <= 6 months only
p.sha6<-ggplot()+ geom_point(data = subset(alphap,age.sample<=6), aes(x = age.sample, y = shannon, group = personid, colour=bf))+
geom_line(data = subset(alphap,age.sample<=6), aes(x = age.sample, y = shannon, group = personid, colour=bf), size=0.3)+
geom_line(data = subset(datfit,age.sample<=6),aes(x = age.sample, y = fit),size = 1)+
geom_ribbon(data = subset(datfit,age.sample<=6),aes(x=age.sample, ymax=ul, ymin=ll), alpha=.5)+
theme(legend.position = "bottom")+
theme(legend.title = element_text(colour="black", size=10))+
labs(color='')+
scale_x_continuous(breaks=seq(from=0,to=24,by=2),
labels=seq(from=0,to=24,by=2))+
theme(legend.text = element_text(colour="black", size = 10))+
theme(legend.key.size = unit(0.5, "cm"))+
theme(legend.position = "bottom",
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
strip.background =element_rect(fill="white"))+
xlab("Infant age (months)") +ylab("Standardized Shannon index")+
facet_grid(. ~ pop)
p.sha6
alphap.nona<-alphap[!is.na(alphap$bf),]
alphap.nona$pop.bf<-paste(alphap.nona$pop,alphap.nona$bf,sep="_")
alphap.nona$pop.bf<-as.factor(alphap.nona$pop.bf)
b2<-gamm(shannon~s(age.sample,by=pop.bf),family=gaussian,
data=alphap.nona,random=list(personid=~1))
pred <- predict(b2$gam, newdata = alphap.nona,se.fit=TRUE)
datfit<-cbind(alphap.nona, fit=pred$fit,ul=(pred$fit+(1.96*pred$se.fit)),ll=(pred$fit-(1.96*pred$se.fit)))
Samples <=6 months only
p.sha.rm.bf6<-ggplot()+ geom_point(data = subset(alphap.nona,age.sample<=6), aes(x = age.sample, y = shannon, group = personid, colour=bf), size=1)+ #, colour=bf
geom_line(data = subset(alphap.nona,age.sample<=6), aes(x = age.sample, y = shannon, group = personid, colour=bf),size=0.1)+ #, colour=bf
geom_line(data = subset(datfit,age.sample<=6),aes(x = age.sample, y = fit, colour=bf),size = 1)+
geom_ribbon(data = subset(datfit,age.sample<=6),aes(x=age.sample, ymax=ul, ymin=ll, fill=bf), alpha=.4)+ guides(fill=FALSE)+
theme(legend.position = "bottom")+
theme(legend.title = element_text(colour="black", size=10))+
labs(color='')+
scale_x_continuous(breaks=seq(from=0,to=24,by=2),
labels=seq(from=0,to=24,by=2))+
theme(legend.text = element_text(colour="black", size = 10))+
theme(legend.key.size = unit(0.5, "cm"))+
theme(legend.position = "bottom",
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
strip.background =element_rect(fill="white"))+
xlab("Infant age (months)") +ylab("Standardized Shannon index")+
facet_grid(. ~ pop)
p.sha.rm.bf6
Subramanian (Bangladesh) data only.
samfile<-merge(samde, he50[,c("child.id","gender","month.exbf","month.food")],by="child.id")
samfile$age.sample<-samfile$age.months
samfile$bf<-factor(samfile$bf,levels=c("ExclusiveBF","Non_exclusiveBF","No_BF"))
samfile$personid<-as.factor(paste("rm", tolower(samfile$child.id),sep="."))
samfile$sampleid<- paste("rm",tolower(samfile$fecal.sample.id),sep=".")
samfile$author<-"Subramanian et al"
samfile$year<-"2014"
samfile$pop<-"Bangladesh"
Shannon index.
alpha.m.rm<-alpha.m.rm %>% group_by(personid) %>% arrange(personid,age.sample) %>%
mutate(month.food6=cut(month.food, breaks=c(-Inf, 6, Inf), labels=c("<=6 months",">6 months")),
month.food5=cut(month.food, breaks=c(-Inf, 5, Inf), labels=c("<=5 months",">5 months")),
month.food4=cut(month.food, breaks=c(-Inf, 4, Inf), labels=c("<=4 months",">4 months")),
month.foodr=as.factor(sort(round(month.food,0))),
month.exbf3=cut(month.exbf, breaks=c(-Inf, 3, Inf), labels=c("<=3 months",">3 months")),
month.exbf2=cut(month.exbf, breaks=c(-Inf, 2, Inf), labels=c("<=2 months",">2 months")),
month.exbf1=cut(month.exbf, breaks=c(-Inf, 1, Inf), labels=c("<=1 months",">1 months")),
month.exbfr=as.factor(sort(round(month.exbf,0))))
ggplot(data=alpha.m.rm,aes(x=age.sample, y=shannon, colour=month.exbf2, group=personid)) +geom_point()+geom_line() +
geom_smooth(data=alpha.m.rm,aes(x=age.sample, y=shannon,group=month.exbf2))
For samples <=6 months old only.
chao1.nebf <- metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="chao1"),sm="RD", backtransf=FALSE)
forest(chao1.nebf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="chao1")$pop)
chao1.nebf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0677 [-0.0136; 0.1491] 79.0
Azad et al 2015 ( Canada ) 0.3752 [ 0.0596; 0.6908] 5.3
Bender et al 2016 ( Haiti ) 0.4922 [-0.3266; 1.3110] 0.8
Wood et al 2017 ( South Africa ) 0.5113 [ 0.0923; 0.9303] 3.0
Pannaraj et al 2017 ( USA(CA_FL) ) 0.1201 [-0.1362; 0.3764] 8.0
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.6457 [ 0.2430; 1.0485] 3.2
Thompson et al 2015 ( USA(NC) ) 0.4111 [-0.4054; 1.2277] 0.8
%W(random)
Subramanian et al 2014 ( Bangladesh ) 28.0
Azad et al 2015 ( Canada ) 16.8
Bender et al 2016 ( Haiti ) 4.9
Wood et al 2017 ( South Africa ) 12.6
Pannaraj et al 2017 ( USA(CA_FL) ) 19.6
Sordillo et al 2017 ( USA(CA_MA_MO) ) 13.2
Thompson et al 2015 ( USA(NC) ) 4.9
Number of studies combined: k = 7
RD 95%-CI z p-value
Fixed effect model 0.1259 [0.0536; 0.1982] 3.41 0.0006
Random effects model 0.2995 [0.1020; 0.4969] 2.97 0.0030
Quantifying heterogeneity:
tau^2 = 0.0346; H = 1.59 [1.05; 2.41]; I^2 = 60.7% [9.9%; 82.8%]
Test of heterogeneity:
Q d.f. p-value
15.25 6 0.0184
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=chao1.nebf$studlab,pval=chao1.nebf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.102847325927163 |
| Azad et al 2015 ( Canada ) | 0.01980051110656 |
| Bender et al 2016 ( Haiti ) | 0.238756122389415 |
| Wood et al 2017 ( South Africa ) | 0.0167677830041401 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.358399385859461 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 0.00167635766494574 |
| Thompson et al 2015 ( USA(NC) ) | 0.32371285335117 |
os.nebf <- metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="observed_species"),sm="RD", backtransf=FALSE)
forest(os.nebf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="observed_species")$pop)
os.nebf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0673 [-0.0127; 0.1473] 77.1
Azad et al 2015 ( Canada ) 0.3721 [ 0.0590; 0.6852] 5.0
Bender et al 2016 ( Haiti ) 0.4601 [-0.3413; 1.2616] 0.8
Wood et al 2017 ( South Africa ) 0.4996 [ 0.0848; 0.9143] 2.9
Pannaraj et al 2017 ( USA(CA_FL) ) 0.1447 [-0.0763; 0.3657] 10.1
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.6461 [ 0.2454; 1.0467] 3.1
Thompson et al 2015 ( USA(NC) ) 0.3555 [-0.3448; 1.0557] 1.0
%W(random)
Subramanian et al 2014 ( Bangladesh ) 28.2
Azad et al 2015 ( Canada ) 16.1
Bender et al 2016 ( Haiti ) 4.5
Wood et al 2017 ( South Africa ) 12.0
Pannaraj et al 2017 ( USA(CA_FL) ) 21.0
Sordillo et al 2017 ( USA(CA_MA_MO) ) 12.5
Thompson et al 2015 ( USA(NC) ) 5.7
Number of studies combined: k = 7
RD 95%-CI z p-value
Fixed effect model 0.1266 [0.0564; 0.1969] 3.53 0.0004
Random effects model 0.2909 [0.1053; 0.4765] 3.07 0.0021
Quantifying heterogeneity:
tau^2 = 0.0301; H = 1.59 [1.05; 2.41]; I^2 = 60.4% [9.1%; 82.7%]
Test of heterogeneity:
Q d.f. p-value
15.14 6 0.0192
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=os.nebf$studlab,pval=os.nebf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.0990100956520888 |
| Azad et al 2015 ( Canada ) | 0.019839103185688 |
| Bender et al 2016 ( Haiti ) | 0.260501865374435 |
| Wood et al 2017 ( South Africa ) | 0.0182288177251842 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.19952096913775 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 0.00157427402976728 |
| Thompson et al 2015 ( USA(NC) ) | 0.319751771051308 |
pwt.nebf <- metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="pd_whole_tree"),sm="RD", backtransf=FALSE)
forest(pwt.nebf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="pd_whole_tree")$pop)
pwt.nebf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0953 [ 0.0196; 0.1710] 80.5
Azad et al 2015 ( Canada ) 0.2877 [-0.0327; 0.6082] 4.5
Bender et al 2016 ( Haiti ) 0.3082 [-0.3766; 0.9931] 1.0
Wood et al 2017 ( South Africa ) 0.4724 [ 0.0489; 0.8958] 2.6
Pannaraj et al 2017 ( USA(CA_FL) ) 0.2168 [ 0.0121; 0.4215] 11.0
Thompson et al 2015 ( USA(NC) ) 0.7261 [-0.2385; 1.6906] 0.5
%W(random)
Subramanian et al 2014 ( Bangladesh ) 52.8
Azad et al 2015 ( Canada ) 12.0
Bender et al 2016 ( Haiti ) 3.1
Wood et al 2017 ( South Africa ) 7.4
Pannaraj et al 2017 ( USA(CA_FL) ) 23.2
Thompson et al 2015 ( USA(NC) ) 1.6
Number of studies combined: k = 6
RD 95%-CI z p-value
Fixed effect model 0.1322 [0.0643; 0.2001] 3.82 0.0001
Random effects model 0.1910 [0.0685; 0.3134] 3.06 0.0022
Quantifying heterogeneity:
tau^2 = 0.0059; H = 1.15 [1.00; 1.78]; I^2 = 25.0% [0.0%; 68.4%]
Test of heterogeneity:
Q d.f. p-value
6.66 5 0.2469
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=pwt.nebf$studlab,pval=pwt.nebf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.0136266394489582 |
| Azad et al 2015 ( Canada ) | 0.0784131649118116 |
| Bender et al 2016 ( Haiti ) | 0.377692577417145 |
| Wood et al 2017 ( South Africa ) | 0.0288048675369895 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.0379096968161372 |
| Thompson et al 2015 ( USA(NC) ) | 0.140133230873501 |
shannon.nebf <- metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="shannon"),sm="RD", backtransf=FALSE)
forest(shannon.nebf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="shannon")$pop)
shannon.nebf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.2592 [ 0.1185; 0.3999] 57.3
Azad et al 2015 ( Canada ) 0.3262 [ 0.0159; 0.6365] 11.8
Bender et al 2016 ( Haiti ) -0.1146 [-0.7954; 0.5662] 2.4
Wood et al 2017 ( South Africa ) 0.3071 [-0.1311; 0.7452] 5.9
Pannaraj et al 2017 ( USA(CA_FL) ) 0.3732 [ 0.0808; 0.6657] 13.3
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.7684 [ 0.3821; 1.1546] 7.6
Thompson et al 2015 ( USA(NC) ) 0.3001 [-0.5308; 1.1310] 1.6
%W(random)
Subramanian et al 2014 ( Bangladesh ) 40.5
Azad et al 2015 ( Canada ) 15.7
Bender et al 2016 ( Haiti ) 4.0
Wood et al 2017 ( South Africa ) 8.9
Pannaraj et al 2017 ( USA(CA_FL) ) 17.2
Sordillo et al 2017 ( USA(CA_MA_MO) ) 11.0
Thompson et al 2015 ( USA(NC) ) 2.7
Number of studies combined: k = 7
RD 95%-CI z p-value
Fixed effect model 0.3153 [0.2088; 0.4219] 5.80 < 0.0001
Random effects model 0.3359 [0.1959; 0.4759] 4.70 < 0.0001
Quantifying heterogeneity:
tau^2 = 0.0074; H = 1.12 [1.00; 1.67]; I^2 = 20.9% [0.0%; 64.3%]
Test of heterogeneity:
Q d.f. p-value
7.59 6 0.2700
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=shannon.nebf$studlab,pval=shannon.nebf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.000305463231694058 |
| Azad et al 2015 ( Canada ) | 0.0393559233405728 |
| Bender et al 2016 ( Haiti ) | 0.74142212926805 |
| Wood et al 2017 ( South Africa ) | 0.169568310346119 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.0123688713005407 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 9.65594918082499e-05 |
| Thompson et al 2015 ( USA(NC) ) | 0.479014124949741 |
Show the results of Shannon indexes only.
shannon.nebf <- metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="shannon"&(pop!="Haiti")),sm="RD", backtransf=FALSE)
forest(shannon.nebf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="shannon"&(pop!="Haiti"))$pop)
shannon.nebf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.2592 [ 0.1185; 0.3999] 58.8
Azad et al 2015 ( Canada ) 0.3262 [ 0.0159; 0.6365] 12.1
Wood et al 2017 ( South Africa ) 0.3071 [-0.1311; 0.7452] 6.1
Pannaraj et al 2017 ( USA(CA_FL) ) 0.3732 [ 0.0808; 0.6657] 13.6
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.7684 [ 0.3821; 1.1546] 7.8
Thompson et al 2015 ( USA(NC) ) 0.3001 [-0.5308; 1.1310] 1.7
%W(random)
Subramanian et al 2014 ( Bangladesh ) 45.8
Azad et al 2015 ( Canada ) 15.5
Wood et al 2017 ( South Africa ) 8.5
Pannaraj et al 2017 ( USA(CA_FL) ) 17.1
Sordillo et al 2017 ( USA(CA_MA_MO) ) 10.6
Thompson et al 2015 ( USA(NC) ) 2.5
Number of studies combined: k = 6
RD 95%-CI z p-value
Fixed effect model 0.3261 [0.2183; 0.4340] 5.93 < 0.0001
Random effects model 0.3483 [0.2145; 0.4821] 5.10 < 0.0001
Quantifying heterogeneity:
tau^2 = 0.0050; H = 1.10 [1.00; 1.61]; I^2 = 16.9% [0.0%; 61.6%]
Test of heterogeneity:
Q d.f. p-value
6.02 5 0.3047
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=shannon.nebf$studlab,pval=shannon.nebf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.000305463231694058 |
| Azad et al 2015 ( Canada ) | 0.0393559233405728 |
| Wood et al 2017 ( South Africa ) | 0.169568310346119 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.0123688713005407 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 9.65594918082499e-05 |
| Thompson et al 2015 ( USA(NC) ) | 0.479014124949741 |
shannon.nebf <- metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="shannon"&(pop!="USA(NC)")),sm="RD", backtransf=FALSE)
forest(shannon.nebf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="shannon"&(pop!="USA(NC)"))$pop)
shannon.nebf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.2592 [ 0.1185; 0.3999] 58.3
Azad et al 2015 ( Canada ) 0.3262 [ 0.0159; 0.6365] 12.0
Bender et al 2016 ( Haiti ) -0.1146 [-0.7954; 0.5662] 2.5
Wood et al 2017 ( South Africa ) 0.3071 [-0.1311; 0.7452] 6.0
Pannaraj et al 2017 ( USA(CA_FL) ) 0.3732 [ 0.0808; 0.6657] 13.5
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.7684 [ 0.3821; 1.1546] 7.7
%W(random)
Subramanian et al 2014 ( Bangladesh ) 36.5
Azad et al 2015 ( Canada ) 17.2
Bender et al 2016 ( Haiti ) 4.9
Wood et al 2017 ( South Africa ) 10.3
Pannaraj et al 2017 ( USA(CA_FL) ) 18.6
Sordillo et al 2017 ( USA(CA_MA_MO) ) 12.6
Number of studies combined: k = 6
RD 95%-CI z p-value
Fixed effect model 0.3156 [0.2082; 0.4230] 5.76 < 0.0001
Random effects model 0.3427 [0.1851; 0.5004] 4.26 < 0.0001
Quantifying heterogeneity:
tau^2 = 0.0126; H = 1.23 [1.00; 1.94]; I^2 = 34.1% [0.0%; 73.5%]
Test of heterogeneity:
Q d.f. p-value
7.58 5 0.1807
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=shannon.nebf$studlab,pval=shannon.nebf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.000305463231694058 |
| Azad et al 2015 ( Canada ) | 0.0393559233405728 |
| Bender et al 2016 ( Haiti ) | 0.74142212926805 |
| Wood et al 2017 ( South Africa ) | 0.169568310346119 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.0123688713005407 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 9.65594918082499e-05 |
shannon.nebf <- metagen(estimate.nebf, se.nebf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="shannon"&(pop!="USA(CA_MA_MO)")),sm="RD", backtransf=FALSE)
forest(shannon.nebf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="shannon"&(pop!="USA(CA_MA_MO)"))$pop)
shannon.nebf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.2592 [ 0.1185; 0.3999] 62.0
Azad et al 2015 ( Canada ) 0.3262 [ 0.0159; 0.6365] 12.8
Bender et al 2016 ( Haiti ) -0.1146 [-0.7954; 0.5662] 2.7
Wood et al 2017 ( South Africa ) 0.3071 [-0.1311; 0.7452] 6.4
Pannaraj et al 2017 ( USA(CA_FL) ) 0.3732 [ 0.0808; 0.6657] 14.4
Thompson et al 2015 ( USA(NC) ) 0.3001 [-0.5308; 1.1310] 1.8
%W(random)
Subramanian et al 2014 ( Bangladesh ) 62.0
Azad et al 2015 ( Canada ) 12.8
Bender et al 2016 ( Haiti ) 2.7
Wood et al 2017 ( South Africa ) 6.4
Pannaraj et al 2017 ( USA(CA_FL) ) 14.4
Thompson et al 2015 ( USA(NC) ) 1.8
Number of studies combined: k = 6
RD 95%-CI z p-value
Fixed effect model 0.2780 [0.1672; 0.3889] 4.92 < 0.0001
Random effects model 0.2780 [0.1672; 0.3889] 4.92 < 0.0001
Quantifying heterogeneity:
tau^2 = 0; H = 1.00 [1.00; 1.21]; I^2 = 0.0% [0.0%; 32.0%]
Test of heterogeneity:
Q d.f. p-value
1.87 5 0.8674
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=shannon.nebf$studlab,pval=shannon.nebf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.000305463231694058 |
| Azad et al 2015 ( Canada ) | 0.0393559233405728 |
| Bender et al 2016 ( Haiti ) | 0.74142212926805 |
| Wood et al 2017 ( South Africa ) | 0.169568310346119 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.0123688713005407 |
| Thompson et al 2015 ( USA(NC) ) | 0.479014124949741 |
Show results of Shannon index only.
load(paste(dir,"data/alphasum.vagcs.rda",sep=""))
shannon.nebf <- metagen(estimate.nebf, se.nebf, studlab=study,data=subset(alphasum.vag, index=="shannon"),sm="RD", backtransf=FALSE)
forest(shannon.nebf,smlab="Standardized \n diversity difference")
shannon.nebf
RD 95%-CI %W(fixed)
Azad et al 2015 (Canada) 0.1424 [-0.0984; 0.3832] 40.8
Bender et al 2016 (Haiti) -0.2018 [-0.9752; 0.5715] 4.0
Pannaraj et al 2017 (USA(CA_FL)) 0.3734 [ 0.1294; 0.6174] 39.7
Sordillo et al 2017 (USA(CA_MA_MO)) 0.4321 [ 0.0420; 0.8223] 15.5
%W(random)
Azad et al 2015 (Canada) 38.3
Bender et al 2016 (Haiti) 5.4
Pannaraj et al 2017 (USA(CA_FL)) 37.6
Sordillo et al 2017 (USA(CA_MA_MO)) 18.6
Number of studies combined: k = 4
RD 95%-CI z p-value
Fixed effect model 0.2656 [0.1118; 0.4194] 3.38 0.0007
Random effects model 0.2647 [0.0798; 0.4495] 2.81 0.0050
Quantifying heterogeneity:
tau^2 = 0.0081; H = 1.13 [1.00; 2.90]; I^2 = 22.3% [0.0%; 88.1%]
Test of heterogeneity:
Q d.f. p-value
3.86 3 0.2771
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=shannon.nebf$studlab,pval=shannon.nebf$pval))
| study | pval |
|---|---|
| Azad et al 2015 (Canada) | 0.246532761397953 |
| Bender et al 2016 (Haiti) | 0.608996225108868 |
| Pannaraj et al 2017 (USA(CA_FL)) | 0.00270151112577258 |
| Sordillo et al 2017 (USA(CA_MA_MO)) | 0.0299341350698865 |
shannon.nebf <- metagen(estimate.nebf, se.nebf, studlab=study,data=subset(alphasum.cs, index=="shannon"),sm="RD", backtransf=FALSE)
forest(shannon.nebf,smlab="Standardized \n diversity difference")
shannon.nebf
RD 95%-CI %W(fixed)
Azad et al 2015 (Canada) 0.4873 [ 0.0230; 0.9515] 40.3
Pannaraj et al 2017 (USA(CA_FL)) -0.0737 [-0.5869; 0.4395] 33.0
Sordillo et al 2017 (USA(CA_MA_MO)) 1.0833 [ 0.5127; 1.6540] 26.7
%W(random)
Azad et al 2015 (Canada) 34.9
Pannaraj et al 2017 (USA(CA_FL)) 33.4
Sordillo et al 2017 (USA(CA_MA_MO)) 31.7
Number of studies combined: k = 3
RD 95%-CI z p-value
Fixed effect model 0.4612 [ 0.1665; 0.7560] 3.07 0.0022
Random effects model 0.4888 [-0.1327; 1.1103] 1.54 0.1232
Quantifying heterogeneity:
tau^2 = 0.2323; H = 2.09 [1.16; 3.77]; I^2 = 77.1% [25.6%; 93.0%]
Test of heterogeneity:
Q d.f. p-value
8.75 2 0.0126
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=shannon.nebf$studlab,pval=shannon.nebf$pval))
| study | pval |
|---|---|
| Azad et al 2015 (Canada) | 0.0396622679428872 |
| Pannaraj et al 2017 (USA(CA_FL)) | 0.778417002121859 |
| Sordillo et al 2017 (USA(CA_MA_MO)) | 0.000198560001433614 |
a.nebf<-ggplot(data=a.metatab.r,aes(x=estimate.nebf,y=index))+
geom_point(shape=17, colour="red")+
geom_errorbarh(aes(xmin=ll.nebf,xmax=ul.nebf),height=0.0, colour="red")+
scale_x_continuous(breaks=seq(from=0,to=1,by=0.2),
labels=seq(from=0,to=1,by=0.2))+
geom_vline(xintercept=0,linetype="dashed")+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())+
ggtitle("Non-EBF vs. EBF")+
xlab("Pooled Standardized diversity difference")+ylab("Alpha diversity index")
a.nebf
kable(a.metatab.r[,grep(".nebf",colnames(a.metatab.r))])
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| shannon | 0.3359260 | 0.0714283 | 0.1959290 | 0.4759229 | 4.702980 | 0.0000026 | 0.0000103 |
| observed_species | 0.2908894 | 0.0946918 | 0.1052969 | 0.4764819 | 3.071960 | 0.0021266 | 0.0029552 |
| pd_whole_tree | 0.1909514 | 0.0624970 | 0.0684595 | 0.3134432 | 3.055369 | 0.0022478 | 0.0029552 |
| chao1 | 0.2994547 | 0.1007464 | 0.1019953 | 0.4969141 | 2.972360 | 0.0029552 | 0.0029552 |
chao1.conbf <- metagen(estimate.conbf, se.conbf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="chao1"),sm="RD", backtransf=FALSE)
forest(chao1.conbf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="chao1")$pop)
chao1.conbf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0550 [-0.0189; 0.1289] 65.2
Azad et al 2015 ( Canada ) 0.5725 [ 0.3986; 0.7464] 11.8
Bender et al 2016 ( Haiti ) NA 0.0
Wood et al 2017 ( South Africa ) NA 0.0
Pannaraj et al 2017 ( USA(CA_FL) ) 0.1302 [-0.0599; 0.3202] 9.9
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.4245 [ 0.2553; 0.5937] 12.4
Thompson et al 2015 ( USA(NC) ) 0.4233 [-0.2687; 1.1153] 0.7
%W(random)
Subramanian et al 2014 ( Bangladesh ) 24.7
Azad et al 2015 ( Canada ) 22.4
Bender et al 2016 ( Haiti ) 0.0
Wood et al 2017 ( South Africa ) 0.0
Pannaraj et al 2017 ( USA(CA_FL) ) 21.9
Sordillo et al 2017 ( USA(CA_MA_MO) ) 22.6
Thompson et al 2015 ( USA(NC) ) 8.3
Number of studies combined: k = 5
RD 95%-CI z p-value
Fixed effect model 0.1720 [0.1123; 0.2316] 5.65 < 0.0001
Random effects model 0.3016 [0.0580; 0.5451] 2.43 0.0152
Quantifying heterogeneity:
tau^2 = 0.0610; H = 3.13 [2.19; 4.49]; I^2 = 89.8% [79.1%; 95.0%]
Test of heterogeneity:
Q d.f. p-value
39.26 4 < 0.0001
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=chao1.conbf$studlab,pval=chao1.conbf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.144460866036347 |
| Azad et al 2015 ( Canada ) | 1.0991009354299e-10 |
| Bender et al 2016 ( Haiti ) | NA |
| Wood et al 2017 ( South Africa ) | NA |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.179455833167877 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 8.77670396356771e-07 |
| Thompson et al 2015 ( USA(NC) ) | 0.230600549600578 |
os.conbf <- metagen(estimate.conbf, se.conbf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="observed_species"),sm="RD", backtransf=FALSE)
forest(os.conbf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="observed_species")$pop)
os.conbf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0603 [-0.0124; 0.1329] 63.5
Azad et al 2015 ( Canada ) 0.5895 [ 0.4168; 0.7623] 11.2
Bender et al 2016 ( Haiti ) NA 0.0
Wood et al 2017 ( South Africa ) NA 0.0
Pannaraj et al 2017 ( USA(CA_FL) ) 0.1163 [-0.0479; 0.2806] 12.4
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.4610 [ 0.2928; 0.6291] 11.9
Thompson et al 2015 ( USA(NC) ) 0.3661 [-0.2275; 0.9598] 1.0
%W(random)
Subramanian et al 2014 ( Bangladesh ) 24.1
Azad et al 2015 ( Canada ) 21.9
Bender et al 2016 ( Haiti ) 0.0
Wood et al 2017 ( South Africa ) 0.0
Pannaraj et al 2017 ( USA(CA_FL) ) 22.1
Sordillo et al 2017 ( USA(CA_MA_MO) ) 22.0
Thompson et al 2015 ( USA(NC) ) 10.0
Number of studies combined: k = 5
RD 95%-CI z p-value
Fixed effect model 0.1772 [0.1192; 0.2351] 6.00 < 0.0001
Random effects model 0.3070 [0.0641; 0.5500] 2.48 0.0133
Quantifying heterogeneity:
tau^2 = 0.0625; H = 3.30 [2.33; 4.69]; I^2 = 90.8% [81.6%; 95.4%]
Test of heterogeneity:
Q d.f. p-value
43.68 4 < 0.0001
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=os.conbf$studlab,pval=os.conbf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.104033167825551 |
| Azad et al 2015 ( Canada ) | 2.26960893212611e-11 |
| Bender et al 2016 ( Haiti ) | NA |
| Wood et al 2017 ( South Africa ) | NA |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.165000455562882 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 7.73447284594136e-08 |
| Thompson et al 2015 ( USA(NC) ) | 0.226684364502839 |
pwt.conbf <- metagen(estimate.conbf, se.conbf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="pd_whole_tree"),sm="RD", backtransf=FALSE)
forest(pwt.conbf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="pd_whole_tree")$pop)
pwt.conbf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0646 [-0.0047; 0.1338] 72.9
Azad et al 2015 ( Canada ) 0.5311 [ 0.3540; 0.7082] 11.2
Bender et al 2016 ( Haiti ) NA 0.0
Wood et al 2017 ( South Africa ) NA 0.0
Pannaraj et al 2017 ( USA(CA_FL) ) 0.2032 [ 0.0523; 0.3541] 15.4
Thompson et al 2015 ( USA(NC) ) 0.8566 [ 0.0509; 1.6623] 0.5
%W(random)
Subramanian et al 2014 ( Bangladesh ) 32.9
Azad et al 2015 ( Canada ) 29.1
Bender et al 2016 ( Haiti ) 0.0
Wood et al 2017 ( South Africa ) 0.0
Pannaraj et al 2017 ( USA(CA_FL) ) 30.2
Thompson et al 2015 ( USA(NC) ) 7.8
Number of studies combined: k = 4
RD 95%-CI z p-value
Fixed effect model 0.1422 [0.0830; 0.2013] 4.71 < 0.0001
Random effects model 0.3037 [0.0475; 0.5598] 2.32 0.0202
Quantifying heterogeneity:
tau^2 = 0.0506; H = 3.00 [1.97; 4.57]; I^2 = 88.9% [74.2%; 95.2%]
Test of heterogeneity:
Q d.f. p-value
26.99 3 < 0.0001
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=pwt.conbf$studlab,pval=pwt.conbf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.0676383461236607 |
| Azad et al 2015 ( Canada ) | 4.17049539317814e-09 |
| Bender et al 2016 ( Haiti ) | NA |
| Wood et al 2017 ( South Africa ) | NA |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.00831447939658577 |
| Thompson et al 2015 ( USA(NC) ) | 0.0371791605971582 |
shannon.conbf <- metagen(estimate.conbf, se.conbf, studlab=paste(author,year,"(",pop,")"),data=subset(alphaall,index=="shannon"&(!pop %in% c("Haiti","South Africa"))),sm="RD", backtransf=FALSE)
forest(shannon.conbf,smlab="Standardized \n diversity difference",sortvar=subset(alphaall,index=="shannon"&(!pop %in% c("Haiti","South Africa")))$pop)
shannon.conbf
RD 95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.2131 [ 0.0843; 0.3420] 39.0
Azad et al 2015 ( Canada ) 0.5872 [ 0.4153; 0.7590] 21.9
Pannaraj et al 2017 ( USA(CA_FL) ) 0.2570 [ 0.0375; 0.4765] 13.5
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.5660 [ 0.4038; 0.7282] 24.6
Thompson et al 2015 ( USA(NC) ) -0.0180 [-0.8408; 0.8049] 1.0
%W(random)
Subramanian et al 2014 ( Bangladesh ) 25.8
Azad et al 2015 ( Canada ) 23.8
Pannaraj et al 2017 ( USA(CA_FL) ) 21.4
Sordillo et al 2017 ( USA(CA_MA_MO) ) 24.2
Thompson et al 2015 ( USA(NC) ) 4.8
Number of studies combined: k = 5
RD 95%-CI z p-value
Fixed effect model 0.3858 [0.3053; 0.4663] 9.39 < 0.0001
Random effects model 0.3857 [0.1872; 0.5843] 3.81 0.0001
Quantifying heterogeneity:
tau^2 = 0.0355; H = 2.19 [1.42; 3.37]; I^2 = 79.1% [50.4%; 91.2%]
Test of heterogeneity:
Q d.f. p-value
19.16 4 0.0007
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
kable(cbind(study=shannon.conbf$studlab,pval=shannon.conbf$pval))
| study | pval |
|---|---|
| Subramanian et al 2014 ( Bangladesh ) | 0.00119013497616748 |
| Azad et al 2015 ( Canada ) | 2.14964602978342e-11 |
| Pannaraj et al 2017 ( USA(CA_FL) ) | 0.0217486284946625 |
| Sordillo et al 2017 ( USA(CA_MA_MO) ) | 7.9318409863511e-12 |
| Thompson et al 2015 ( USA(NC) ) | 0.965887658685873 |
a.conbf<-ggplot(data=a.metatab.r,aes(x=estimate.conbf,y=index))+
geom_point(shape=17, colour="red")+
geom_errorbarh(aes(xmin=ll.conbf,xmax=ul.conbf),height=0.0, colour="red")+
geom_vline(xintercept=0,linetype="dashed")+
scale_x_continuous(breaks=seq(from=0,to=1,by=0.2),
labels=seq(from=0,to=1,by=0.2))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())+
ggtitle("Trend across EBF, non-EBF, non-BF")+
xlab("Pooled standardized diversity difference")+ylab("Alpha diversity index")
a.conbf
kable(a.metatab.r[,grep(".conbf",colnames(a.metatab.r))])
| estimate.conbf | se.conbf | ll.conbf | ul.conbf | z.conbf | p.conbf | p.adjust.conbf | |
|---|---|---|---|---|---|---|---|
| shannon | 0.3857475 | 0.1012964 | 0.1872101 | 0.5842849 | 3.808105 | 0.0001400 | 0.0005601 |
| observed_species | 0.3070476 | 0.1239712 | 0.0640686 | 0.5500267 | 2.476766 | 0.0132579 | 0.0201585 |
| pd_whole_tree | 0.3036608 | 0.1306976 | 0.0474982 | 0.5598233 | 2.323384 | 0.0201585 | 0.0201585 |
| chao1 | 0.3015700 | 0.1242513 | 0.0580420 | 0.5450980 | 2.427098 | 0.0152201 | 0.0201585 |
Results of 7 studies.
For samples <= 6 months old only in all studies (note for USA(NC) study: GAMLSS BEZI with random subject effect could not run on very small sample size=> did not include subject random effect). Results of random meta-analysis models for taxa available in at least >50% of studies based on adjusted estimates and standard errors from GAMLSS models with zero-inflated beta family adjusted for infant age at sample collection.
load(paste(dir,"data/metatab.zi7.rda",sep=""))
GAMLSS models with zero-inflated beta family
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,plot="heatmap",fill.value="log(OR)")
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,plot="forest",fill.value="log(OR)")
kable(metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes | 0.2477163 | 0.0684909 | 0.1134765 | 0.3819560 | 3.616776 | 0.0002983 | 0.0017898 |
| k__bacteria.p__bacteroidetes | 0.2079683 | 0.0756683 | 0.0596613 | 0.3562754 | 2.748422 | 0.0059883 | 0.0179648 |
Nice plot
metadat<-metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="main",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=8,forest.axis.text.x=8)
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,plot="heatmap",fill.value="log(OR)")
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,plot="forest",fill.value="log(OR)")
kable(metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales | 0.3015251 | 0.0904382 | 0.1242695 | 0.4787808 | 3.334046 | 0.0008559 | 0.0105564 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales | 0.2100511 | 0.0757620 | 0.0615603 | 0.3585419 | 2.772512 | 0.0055625 | 0.0369277 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales | 0.2005008 | 0.0896354 | 0.0248186 | 0.3761830 | 2.236847 | 0.0252963 | 0.1169955 |
Nice plot
metadat<-metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=8,forest.axis.text.x=8)
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,plot="heatmap",fill.value="log(OR)")
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,plot="forest",fill.value="log(OR)")
kable(metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae | 0.2311548 | 0.0856673 | 0.0632499 | 0.3990596 | 2.698284 | 0.0069698 | 0.0756721 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae | 0.2107748 | 0.0807013 | 0.0526031 | 0.3689465 | 2.611789 | 0.0090070 | 0.0855663 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae | 0.2005008 | 0.0896354 | 0.0248186 | 0.3761830 | 2.236847 | 0.0252963 | 0.1747746 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae | 0.1652266 | 0.0841374 | 0.0003203 | 0.3301328 | 1.963771 | 0.0495566 | 0.3086619 |
Nice plot
metadat<-metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=8,forest.axis.text.x=8)
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,plot="heatmap",fill.value="log(OR)")
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,plot="forest",fill.value="log(OR)")
kable(metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. | 0.3926058 | 0.1237182 | 0.1501225 | 0.6350890 | 3.173387 | 0.0015067 | 0.0561251 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__megasphaera | 0.4000827 | 0.1416041 | 0.1225438 | 0.6776217 | 2.825361 | 0.0047227 | 0.1115318 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides | 0.2107748 | 0.0807013 | 0.0526031 | 0.3689465 | 2.611789 | 0.0090070 | 0.1220036 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__clostridium | 0.3454360 | 0.1505929 | 0.0502793 | 0.6405927 | 2.293839 | 0.0217997 | 0.2512768 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__veillonella | 0.2136555 | 0.1033136 | 0.0111645 | 0.4161465 | 2.068028 | 0.0386374 | 0.3598107 |
Nice plot
metadat<-metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=8,forest.axis.text.x=8)
load(paste(dir,"data/metatab.zi.nounc7.rda",sep=""))
kable(metatab.show(metatab=metatab.zi.nounc$random,taxacom.pooled.tab=taxacom.zi.nounc,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes | 0.2469023 | 0.0691970 | 0.1112786 | 0.3825259 | 3.568106 | 0.0003596 | 0.0025170 |
| k__bacteria.p__bacteroidetes | 0.1932696 | 0.0763734 | 0.0435804 | 0.3429588 | 2.530586 | 0.0113872 | 0.0398552 |
kable(metatab.show(metatab=metatab.zi.nounc$random,taxacom.pooled.tab=taxacom.zi.nounc,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales | 0.2819748 | 0.0889258 | 0.1076835 | 0.4562662 | 3.170900 | 0.0015197 | 0.0233017 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales | 0.1953538 | 0.0764693 | 0.0454767 | 0.3452308 | 2.554669 | 0.0106289 | 0.0873019 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales | 0.1990864 | 0.0908867 | 0.0209516 | 0.3772211 | 2.190489 | 0.0284888 | 0.1638107 |
kable(metatab.show(metatab=metatab.zi.nounc$random,taxacom.pooled.tab=taxacom.zi.nounc,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae | 0.1947860 | 0.0815355 | 0.0349794 | 0.3545925 | 2.388972 | 0.0168956 | 0.2092121 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae | 0.2232183 | 0.0940439 | 0.0388955 | 0.4075410 | 2.373553 | 0.0176179 | 0.2092121 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae | 0.1990864 | 0.0908867 | 0.0209516 | 0.3772211 | 2.190489 | 0.0284888 | 0.2460398 |
kable(metatab.show(metatab=metatab.zi.nounc$random,taxacom.pooled.tab=taxacom.zi.nounc,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. | 0.3970460 | 0.1275892 | 0.1469758 | 0.6471162 | 3.111909 | 0.0018588 | 0.0887585 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__megasphaera | 0.4000827 | 0.1416041 | 0.1225438 | 0.6776217 | 2.825361 | 0.0047227 | 0.1804084 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae.g__ | 0.2425407 | 0.0943469 | 0.0576242 | 0.4274571 | 2.570734 | 0.0101483 | 0.2416618 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides | 0.1947860 | 0.0815355 | 0.0349794 | 0.3545925 | 2.388972 | 0.0168956 | 0.2804176 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__clostridium | 0.3454360 | 0.1505929 | 0.0502793 | 0.6405927 | 2.293839 | 0.0217997 | 0.3202883 |
load(paste(dir,"data/metatab.zi.noha7.rda",sep=""))
kable(metatab.show(metatab=metatab.zi.noha$random,taxacom.pooled.tab=taxacom.zi.noha,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes | 0.2585644 | 0.0699441 | 0.1214765 | 0.3956523 | 3.696729 | 0.0002184 | 0.0013104 |
| k__bacteria.p__bacteroidetes | 0.2137713 | 0.0769990 | 0.0628560 | 0.3646866 | 2.776286 | 0.0054984 | 0.0164951 |
kable(metatab.show(metatab=metatab.zi.noha$random,taxacom.pooled.tab=taxacom.zi.noha,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales | 0.3197985 | 0.0708228 | 0.1809883 | 0.4586087 | 4.515471 | 0.0000063 | 0.0001200 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales | 0.2201357 | 0.0770481 | 0.0691242 | 0.3711471 | 2.857121 | 0.0042750 | 0.0324902 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales | 0.1839766 | 0.0915095 | 0.0046213 | 0.3633320 | 2.010465 | 0.0443820 | 0.2108145 |
kable(metatab.show(metatab=metatab.zi.noha$random,taxacom.pooled.tab=taxacom.zi.noha,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae | 0.2061111 | 0.0822865 | 0.0448325 | 0.3673898 | 2.504798 | 0.0122521 | 0.1347734 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae | 0.2060989 | 0.0969374 | 0.0161050 | 0.3960927 | 2.126103 | 0.0334947 | 0.2946724 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae | 0.1839766 | 0.0915095 | 0.0046213 | 0.3633320 | 2.010465 | 0.0443820 | 0.2946724 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae | 0.1733438 | 0.0868412 | 0.0031382 | 0.3435495 | 1.996101 | 0.0459230 | 0.2946724 |
kable(metatab.show(metatab=metatab.zi.noha$random,taxacom.pooled.tab=taxacom.zi.noha,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__megasphaera | 0.4521725 | 0.1519683 | 0.1543202 | 0.7500249 | 2.975441 | 0.0029257 | 0.1062738 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. | 0.3594531 | 0.1297330 | 0.1051810 | 0.6137251 | 2.770714 | 0.0055934 | 0.1062738 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides | 0.2061111 | 0.0822865 | 0.0448325 | 0.3673898 | 2.504798 | 0.0122521 | 0.1862323 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__clostridium | 0.3557799 | 0.1521514 | 0.0575687 | 0.6539911 | 2.338328 | 0.0193702 | 0.2676612 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__coprococcus | 0.5201042 | 0.2401244 | 0.0494691 | 0.9907393 | 2.165979 | 0.0303128 | 0.3839625 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae.g__ | 0.5344090 | 0.2586130 | 0.0275369 | 1.0412811 | 2.066443 | 0.0387867 | 0.3877940 |
load(paste(dir,"data/metatab.zi.nohav7.rda",sep=""))
kable(metatab.show(metatab=metatab.zi.nohav$random,taxacom.pooled.tab=taxacom.zi.nohav,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__bacteroidetes | 0.2312551 | 0.0810268 | 0.0724455 | 0.3900646 | 2.854058 | 0.0043165 | 0.0250675 |
| k__bacteria.p__firmicutes | 0.2006141 | 0.0745996 | 0.0544015 | 0.3468267 | 2.689210 | 0.0071621 | 0.0250675 |
kable(metatab.show(metatab=metatab.zi.nohav$random,taxacom.pooled.tab=taxacom.zi.nohav,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales | 0.2337644 | 0.0811408 | 0.0747312 | 0.3927975 | 2.880970 | 0.0039645 | 0.0549097 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales | 0.2968923 | 0.1099207 | 0.0814516 | 0.5123329 | 2.700967 | 0.0069138 | 0.0549097 |
| k__bacteria.p__actinobacteria.c__actinobacteria.o__actinomycetales | -0.1557879 | 0.0644216 | -0.2820518 | -0.0295239 | -2.418256 | 0.0155951 | 0.1024820 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales | 0.2117009 | 0.0993562 | 0.0169663 | 0.4064356 | 2.130727 | 0.0331117 | 0.1692374 |
| k__bacteria.p__firmicutes.c__bacilli.o__bacillales | -0.2108602 | 0.1040509 | -0.4147961 | -0.0069242 | -2.026511 | 0.0427125 | 0.1964774 |
kable(metatab.show(metatab=metatab.zi.nohav$random,taxacom.pooled.tab=taxacom.zi.nohav,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae | 0.2996094 | 0.0741081 | 0.1543601 | 0.4448586 | 4.042869 | 0.0000528 | 0.0050161 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae | 0.2375149 | 0.0870795 | 0.0668422 | 0.4081877 | 2.727563 | 0.0063804 | 0.0850503 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae | 0.2117009 | 0.0993562 | 0.0169663 | 0.4064356 | 2.130727 | 0.0331117 | 0.2621341 |
kable(metatab.show(metatab=metatab.zi.nohav$random,taxacom.pooled.tab=taxacom.zi.nohav,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__acidaminococcus | 2.0640609 | 0.5761745 | 0.9347797 | 3.1933421 | 3.582354 | 0.0003405 | 0.0306461 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. | 0.4120255 | 0.1362642 | 0.1449526 | 0.6790984 | 3.023725 | 0.0024968 | 0.1074319 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__megasphaera | 0.4531996 | 0.1618768 | 0.1359269 | 0.7704723 | 2.799657 | 0.0051157 | 0.1074319 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides | 0.2375149 | 0.0870795 | 0.0668422 | 0.4081877 | 2.727563 | 0.0063804 | 0.1074319 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__veillonella | 0.2611018 | 0.1085724 | 0.0483038 | 0.4738997 | 2.404864 | 0.0161785 | 0.2080092 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__clostridium | 0.3454360 | 0.1505929 | 0.0502793 | 0.6405927 | 2.293839 | 0.0217997 | 0.2615967 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__coprococcus | 0.5470330 | 0.2571987 | 0.0429329 | 1.0511331 | 2.126889 | 0.0334293 | 0.3166986 |
load(paste(dir,"data/metatab.zi.vagcs.rda",sep=""))
kable(metatab.show(metatab=metatab.zi.vag$random,taxacom.pooled.tab=taxacom.zi.vag,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__proteobacteria | -0.3069577 | 0.1017253 | -0.5063356 | -0.1075798 | -3.017517 | 0.0025486 | 0.0178399 |
metadat<-metatab.show(metatab=metatab.zi.vag$random,taxacom.pooled.tab=taxacom.zi.vag,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="main",phyla.col="rainbow",leg.key.size=0.5,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=8,forest.axis.text.x=8)
kable(metatab.show(metatab=metatab.zi.vag$random,taxacom.pooled.tab=taxacom.zi.vag,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales | -0.2990541 | 0.1074593 | -0.5096705 | -0.0884377 | -2.782951 | 0.0053867 | 0.1158139 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales | 0.2827724 | 0.1232153 | 0.0412747 | 0.5242700 | 2.294944 | 0.0217363 | 0.2336654 |
metadat<-metatab.show(metatab=metatab.zi.vag$random,taxacom.pooled.tab=taxacom.zi.vag,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",phyla.col="rainbow",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=8,forest.axis.text.x=8)
kable(metatab.show(metatab=metatab.zi.vag$random,taxacom.pooled.tab=taxacom.zi.vag,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__bacilli.o__bacillales.f__staphylococcaceae | -0.3327091 | 0.1174047 | -0.5628181 | -0.1026001 | -2.833865 | 0.0045989 | 0.1157163 |
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales.f__enterobacteriaceae | -0.2990541 | 0.1074593 | -0.5096705 | -0.0884377 | -2.782951 | 0.0053867 | 0.1157163 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae | 0.3016820 | 0.1105580 | 0.0849923 | 0.5183717 | 2.728721 | 0.0063580 | 0.1157163 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__eubacteriaceae | 0.8045605 | 0.3139899 | 0.1891515 | 1.4199695 | 2.562377 | 0.0103958 | 0.1576704 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae | 0.2827724 | 0.1232153 | 0.0412747 | 0.5242700 | 2.294944 | 0.0217363 | 0.2197783 |
metadat<-metatab.show(metatab=metatab.zi.vag$random,taxacom.pooled.tab=taxacom.zi.vag,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",phyla.col="rainbow",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=8,forest.axis.text.x=8)
kable(metatab.show(metatab=metatab.zi.vag$random,taxacom.pooled.tab=taxacom.zi.vag,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__acidaminococcus | 3.3685160 | 0.4300060 | 2.5257196 | 4.2113123 | 7.833648 | 0.0000000 | 0.0000000 |
| k__bacteria.p__firmicutes.c__bacilli.o__bacillales.f__staphylococcaceae.g__staphylococcus | -0.3330596 | 0.1174105 | -0.5631799 | -0.1029393 | -2.836711 | 0.0045581 | 0.1176238 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__eubacteriaceae.g__pseudoramibacter_eubacterium | 0.8762232 | 0.3157991 | 0.2572683 | 1.4951780 | 2.774622 | 0.0055266 | 0.1176238 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__ | 0.3180415 | 0.1163513 | 0.0899972 | 0.5460858 | 2.733460 | 0.0062673 | 0.1176238 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides | 0.3016820 | 0.1105580 | 0.0849923 | 0.5183717 | 2.728721 | 0.0063580 | 0.1176238 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. | 0.5384916 | 0.2026827 | 0.1412408 | 0.9357425 | 2.656820 | 0.0078881 | 0.1326643 |
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales.f__enterobacteriaceae.g__ | -0.2862286 | 0.1148106 | -0.5112532 | -0.0612040 | -2.493051 | 0.0126651 | 0.1802335 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae.g__ | 0.3027671 | 0.1238937 | 0.0599398 | 0.5455944 | 2.443764 | 0.0145349 | 0.1920686 |
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__pasteurellales.f__pasteurellaceae.g__aggregatibacter | 0.5722694 | 0.2477375 | 0.0867128 | 1.0578260 | 2.309983 | 0.0208891 | 0.2234010 |
| k__bacteria.p__firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae.g__lactococcus | 0.5835624 | 0.2676966 | 0.0588868 | 1.1082381 | 2.179940 | 0.0292619 | 0.2849187 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__blautia | 0.3639237 | 0.1848815 | 0.0015626 | 0.7262849 | 1.968416 | 0.0490202 | 0.4534368 |
metadat<-metatab.show(metatab=metatab.zi.vag$random,taxacom.pooled.tab=taxacom.zi.vag,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",phyla.col="rainbow",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=8,forest.axis.text.x=8)
kable(metatab.show(metatab=metatab.zi.cs$random,taxacom.pooled.tab=taxacom.zi.cs,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__proteobacteria | -0.719666 | 0.1708821 | -1.054589 | -0.3847433 | -4.211478 | 2.54e-05 | 0.0001522 |
metadat<-metatab.show(metatab=metatab.zi.cs$random,taxacom.pooled.tab=taxacom.zi.cs,tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="main",phyla.col="rainbow",leg.key.size=0.4,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=8,forest.axis.text.x=8)
kable(metatab.show(metatab=metatab.zi.cs$random,taxacom.pooled.tab=taxacom.zi.cs,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales | -0.5794373 | 0.2324176 | -1.034968 | -0.1239071 | -2.493087 | 0.0126638 | 0.1561867 |
metadat<-metatab.show(metatab=metatab.zi.cs$random,taxacom.pooled.tab=taxacom.zi.cs,tax.lev="l4",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",phyla.col="rainbow",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=8,forest.axis.text.x=8)
kable(metatab.show(metatab=metatab.zi.cs$random,taxacom.pooled.tab=taxacom.zi.cs,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales.f__enterobacteriaceae | -0.5794373 | 0.2324176 | -1.034968 | -0.1239071 | -2.493087 | 0.0126638 | 0.2596077 |
metadat<-metatab.show(metatab=metatab.zi.cs$random,taxacom.pooled.tab=taxacom.zi.cs,tax.lev="l5",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",phyla.col="rainbow",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=8,forest.axis.text.x=8)
kable(metatab.show(metatab=metatab.zi.cs$random,taxacom.pooled.tab=taxacom.zi.cs,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales.f__enterobacteriaceae.g__proteus | -0.2578897 | 0.0008395 | -0.2595351 | -0.2562442 | -307.189026 | 0.0000000 | 0.0000000 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__ruminococcaceae.g__anaerotruncus | -2.9244379 | 0.7060795 | -4.3083283 | -1.5405475 | -4.141797 | 0.0000345 | 0.0018723 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__phascolarctobacterium | -1.8840686 | 0.8567398 | -3.5632478 | -0.2048894 | -2.199114 | 0.0278698 | 0.6489685 |
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales.f__enterobacteriaceae.g__ | -0.5225859 | 0.2608359 | -1.0338148 | -0.0113570 | -2.003505 | 0.0451232 | 0.9153914 |
metadat<-metatab.show(metatab=metatab.zi.cs$random,taxacom.pooled.tab=taxacom.zi.cs,tax.lev="l6",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",phyla.col="rainbow",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=8,forest.axis.text.x=8)
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l2",showvar=".conbf",p.cutoff.type="p", p.cutoff=1,plot="heatmap",fill.value="log(OR)")
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l2",showvar=".conbf",p.cutoff.type="p", p.cutoff=1,plot="forest",fill.value="log(OR)")
Significant (pooled p<0.05) only
kable(metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l2",showvar=".conbf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.conbf | se.conbf | ll.conbf | ul.conbf | z.conbf | p.conbf | p.adjust.conbf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes | 0.2891066 | 0.0810164 | 0.1303173 | 0.4478959 | 3.568494 | 0.0003590 | 0.0021542 |
| k__bacteria.p__verrucomicrobia | 0.1902821 | 0.0798894 | 0.0337017 | 0.3468625 | 2.381818 | 0.0172274 | 0.0516822 |
| k__bacteria.p__bacteroidetes | 0.2156141 | 0.0979174 | 0.0236994 | 0.4075287 | 2.201999 | 0.0276654 | 0.0553308 |
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l4",showvar=".conbf",p.cutoff.type="p", p.cutoff=1,plot="heatmap",fill.value="log(OR)")
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l4",showvar=".conbf",p.cutoff.type="p", p.cutoff=1,plot="forest",fill.value="log(OR)")
Significant only
kable(metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l4",showvar=".conbf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.conbf | se.conbf | ll.conbf | ul.conbf | z.conbf | p.conbf | p.adjust.conbf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales | 0.3924806 | 0.0632438 | 0.2685252 | 0.5164361 | 6.205840 | 0.0000000 | 0.0000000 |
| k__bacteria.p__actinobacteria.c__coriobacteriia.o__coriobacteriales | 0.3158688 | 0.0545196 | 0.2090123 | 0.4227252 | 5.793673 | 0.0000000 | 0.0000001 |
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__pasteurellales | -0.2179258 | 0.0645356 | -0.3444131 | -0.0914384 | -3.376832 | 0.0007333 | 0.0045218 |
| k__bacteria.p__firmicutes.c__bacilli.o__bacillales | -0.1731560 | 0.0523488 | -0.2757578 | -0.0705541 | -3.307732 | 0.0009405 | 0.0049715 |
| k__bacteria.p__verrucomicrobia.c__verrucomicrobiae.o__verrucomicrobiales | 0.1902859 | 0.0798894 | 0.0337055 | 0.3468662 | 2.381865 | 0.0172252 | 0.0637414 |
| k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales | 0.2299773 | 0.1084234 | 0.0174714 | 0.4424833 | 2.121104 | 0.0339130 | 0.0965217 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales | 0.1976105 | 0.0958110 | 0.0098243 | 0.3853966 | 2.062503 | 0.0391599 | 0.0965944 |
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l5",showvar=".conbf",p.cutoff.type="p", p.cutoff=1,plot="heatmap",fill.value="log(OR)")
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l5",showvar=".conbf",p.cutoff.type="p", p.cutoff=1,plot="forest",fill.value="log(OR)")
Significant only
kable(metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l5",showvar=".conbf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.conbf | se.conbf | ll.conbf | ul.conbf | z.conbf | p.conbf | p.adjust.conbf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae | 0.3158688 | 0.0545196 | 0.2090123 | 0.4227252 | 5.793673 | 0.0000000 | 0.0000001 |
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__pasteurellales.f__pasteurellaceae | -0.2179258 | 0.0645356 | -0.3444131 | -0.0914384 | -3.376832 | 0.0007333 | 0.0069659 |
| k__bacteria.p__firmicutes.c__bacilli.o__bacillales.f__staphylococcaceae | -0.1841218 | 0.0598847 | -0.3014937 | -0.0667498 | -3.074603 | 0.0021078 | 0.0160195 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__peptostreptococcaceae | 0.1890903 | 0.0716934 | 0.0485738 | 0.3296068 | 2.637485 | 0.0083523 | 0.0577070 |
| k__bacteria.p__verrucomicrobia.c__verrucomicrobiae.o__verrucomicrobiales.f__verrucomicrobiaceae | 0.1902859 | 0.0798894 | 0.0337055 | 0.3468662 | 2.381865 | 0.0172252 | 0.0872855 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__ruminococcaceae | 0.2044635 | 0.0874835 | 0.0329990 | 0.3759280 | 2.337166 | 0.0194305 | 0.0922951 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae | 0.1418926 | 0.0667077 | 0.0111478 | 0.2726374 | 2.127078 | 0.0334136 | 0.1233860 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae | 0.3387166 | 0.1598501 | 0.0254162 | 0.6520170 | 2.118964 | 0.0340935 | 0.1233860 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae | 0.1976105 | 0.0958110 | 0.0098243 | 0.3853966 | 2.062503 | 0.0391599 | 0.1240064 |
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l6",showvar=".conbf",p.cutoff.type="p", p.cutoff=1,plot="heatmap",fill.value="log(OR)")
metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l6",showvar=".conbf",p.cutoff.type="p", p.cutoff=1,plot="forest",fill.value="log(OR)")
Significant only
kable(metatab.show(metatab=metatab.zi$random,taxacom.pooled.tab=taxacom.zi,tax.lev="l6",showvar=".conbf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.conbf | se.conbf | ll.conbf | ul.conbf | z.conbf | p.conbf | p.adjust.conbf | |
|---|---|---|---|---|---|---|---|
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__coprococcus | 0.3299376 | 0.0063390 | 0.3175133 | 0.3423619 | 52.048471 | 0.0000000 | 0.0000000 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__blautia | 0.3815612 | 0.0668433 | 0.2505508 | 0.5125716 | 5.708296 | 0.0000000 | 0.0000002 |
| k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. | 0.3803923 | 0.0787618 | 0.2260221 | 0.5347626 | 4.829656 | 0.0000014 | 0.0000255 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__ | 0.3369517 | 0.0781848 | 0.1837122 | 0.4901911 | 4.309681 | 0.0000163 | 0.0002707 |
| k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__pasteurellales.f__pasteurellaceae.g__haemophilus | -0.2280437 | 0.0652393 | -0.3559103 | -0.1001770 | -3.495496 | 0.0004732 | 0.0064094 |
| k__bacteria.p__firmicutes.c__bacilli.o__bacillales.f__staphylococcaceae.g__staphylococcus | -0.1829793 | 0.0594315 | -0.2994629 | -0.0664956 | -3.078824 | 0.0020782 | 0.0196292 |
| k__bacteria.p__verrucomicrobia.c__verrucomicrobiae.o__verrucomicrobiales.f__verrucomicrobiaceae.g__akkermansia | 0.1907668 | 0.0798897 | 0.0341859 | 0.3473478 | 2.387877 | 0.0169460 | 0.1166765 |
| k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__peptostreptococcaceae.g__ | 0.2910458 | 0.1410017 | 0.0146875 | 0.5674042 | 2.064129 | 0.0390054 | 0.1823383 |
load(paste(dir,"data/pathmetatab7.rda",sep=""))
metatab.show(metatab=pathmetatab.zi$random,taxacom.pooled.tab=pathcom.zi,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,plot="heatmap",fill.value="log(OR)")
metatab.show(metatab=pathmetatab.zi$random,taxacom.pooled.tab=pathcom.zi,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,plot="forest",fill.value="log(OR)")
kable(metatab.show(metatab=pathmetatab.zi$random,taxacom.pooled.tab=pathcom.zi,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Environmental.Information.Processing..Signaling.Molecules.and.Interaction | -0.0481241 | 0.0174642 | -0.0823533 | -0.0138949 | -2.755588 | 0.0058587 | 0.2167706 |
| Genetic.Information.Processing..Transcription | 0.0159785 | 0.0066440 | 0.0029565 | 0.0290005 | 2.404959 | 0.0161743 | 0.2992240 |
Nice plot
metadat<-metatab.show(metatab=pathmetatab.zi$random,taxacom.pooled.tab=pathcom.zi,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=8,forest.axis.text.x=8,heat.text.x.angle=0)
kable(metatab.show(metatab=pathmetatab.zi$random,taxacom.pooled.tab=pathcom.zi,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Metabolism..Carbohydrate.Metabolism..Fructose.and.mannose.metabolism | 0.0748047 | 0.0153469 | 0.0447254 | 0.1048841 | 4.874259 | 0.0000011 | 0.0002436 |
| Cellular.Processes..Transport.and.Catabolism..Peroxisome | -0.0634911 | 0.0150010 | -0.0928925 | -0.0340896 | -4.232451 | 0.0000231 | 0.0025774 |
| Metabolism..Lipid.Metabolism..Fatty.acid.metabolism | -0.0862183 | 0.0256901 | -0.1365700 | -0.0358667 | -3.356095 | 0.0007905 | 0.0587615 |
| Genetic.Information.Processing..Replication.and.Repair..Base.excision.repair | 0.0170264 | 0.0054765 | 0.0062925 | 0.0277602 | 3.108958 | 0.0018775 | 0.0863346 |
| Metabolism..Carbohydrate.Metabolism..Pentose.and.glucuronate.interconversions | 0.0618250 | 0.0201180 | 0.0223944 | 0.1012557 | 3.073116 | 0.0021184 | 0.0863346 |
| Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.ansamycins | 0.0758176 | 0.0248949 | 0.0270245 | 0.1246108 | 3.045504 | 0.0023229 | 0.0863346 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Vitamin.B6.metabolism | -0.0286169 | 0.0096273 | -0.0474861 | -0.0097477 | -2.972464 | 0.0029542 | 0.0941123 |
| Metabolism..Lipid.Metabolism..Fatty.acid.biosynthesis | 0.0415976 | 0.0143916 | 0.0133905 | 0.0698047 | 2.890399 | 0.0038475 | 0.0991170 |
| Genetic.Information.Processing..Translation..Ribosome.biogenesis.in.eukaryotes | -0.0707846 | 0.0245939 | -0.1189877 | -0.0225816 | -2.878143 | 0.0040002 | 0.0991170 |
| Metabolism..Carbohydrate.Metabolism..Pentose.phosphate.pathway | 0.0339486 | 0.0122911 | 0.0098585 | 0.0580387 | 2.762045 | 0.0057441 | 0.1280925 |
| Metabolism..Metabolism.of.Other.Amino.Acids..D.Alanine.metabolism | 0.0230465 | 0.0101136 | 0.0032242 | 0.0428689 | 2.278763 | 0.0226811 | 0.4598087 |
| Unclassified..Genetic.Information.Processing..Protein.folding.and.associated.processing | -0.0110941 | 0.0050162 | -0.0209256 | -0.0012625 | -2.211653 | 0.0269906 | 0.5010248 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…other.enzymes | 0.0306056 | 0.0142804 | 0.0026166 | 0.0585946 | 2.143191 | 0.0320978 | 0.5010248 |
| Organismal.Systems..Endocrine.System..Adipocytokine.signaling.pathway | -0.1040949 | 0.0493909 | -0.2008994 | -0.0072905 | -2.107573 | 0.0350680 | 0.5010248 |
| Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms | 0.0263807 | 0.0125598 | 0.0017639 | 0.0509975 | 2.100403 | 0.0356934 | 0.5010248 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…cytochrome.P450 | -0.0998941 | 0.0476250 | -0.1932373 | -0.0065509 | -2.097515 | 0.0359480 | 0.5010248 |
| Human.Diseases..Infectious.Diseases..Epithelial.cell.signaling.in.Helicobacter.pylori.infection | 0.0476778 | 0.0233794 | 0.0018550 | 0.0935006 | 2.039309 | 0.0414192 | 0.5306485 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Nicotinate.and.nicotinamide.metabolism | -0.0310355 | 0.0154611 | -0.0613386 | -0.0007324 | -2.007333 | 0.0447142 | 0.5306485 |
| Metabolism..Carbohydrate.Metabolism..Amino.sugar.and.nucleotide.sugar.metabolism | 0.0289152 | 0.0146735 | 0.0001557 | 0.0576747 | 1.970575 | 0.0487726 | 0.5306485 |
Nice plot all pathways
metadat<-metatab.show(metatab=pathmetatab.zi$random,taxacom.pooled.tab=pathcom.zi,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=4,forest.axis.text.x=4)
Nice plot significant pathways only (pooled p<0.05)
metadat<-metatab.show(metatab=pathmetatab.zi$random,taxacom.pooled.tab=pathcom.zi,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=5,forest.axis.text.x=6)
Nice plot multiple testing adjusted significant pathways only (adjusted pooled p<0.1)
metadat<-metatab.show(metatab=pathmetatab.zi$random,taxacom.pooled.tab=pathcom.zi,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p.adjust", p.cutoff=0.1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=6.5,forest.axis.text.x=6)
load(paste(dir,"data/pathmetatab.zi.sen.rda",sep=""))
Level 2
kable(metatab.show(metatab=pathmetatab.zi.noha$random,taxacom.pooled.tab=pathcom.zi.noha,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Organismal.Systems..Environmental.Adaptation | 0.0489196 | 0.0154245 | 0.0186881 | 0.0791511 | 3.171549 | 0.0015163 | 0.0561025 |
| Environmental.Information.Processing..Signaling.Molecules.and.Interaction | -0.0502036 | 0.0176101 | -0.0847186 | -0.0156885 | -2.850847 | 0.0043603 | 0.0806654 |
| Genetic.Information.Processing..Transcription | 0.0163666 | 0.0063851 | 0.0038521 | 0.0288811 | 2.563255 | 0.0103696 | 0.1278916 |
| Metabolism..Carbohydrate.Metabolism | 0.0131067 | 0.0066302 | 0.0001118 | 0.0261016 | 1.976820 | 0.0480620 | 0.4445737 |
metadat<-metatab.show(metatab=pathmetatab.zi.noha$random,taxacom.pooled.tab=pathcom.zi.noha,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=7,forest.axis.text.x=7)
Level 3
kable(metatab.show(metatab=pathmetatab.zi.noha$random,taxacom.pooled.tab=pathcom.zi.noha,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Metabolism..Carbohydrate.Metabolism..Fructose.and.mannose.metabolism | 0.0806811 | 0.0162836 | 0.0487659 | 0.1125964 | 4.954756 | 0.0000007 | 0.0001622 |
| Cellular.Processes..Transport.and.Catabolism..Peroxisome | -0.0650783 | 0.0150202 | -0.0945174 | -0.0356393 | -4.332715 | 0.0000147 | 0.0016496 |
| Metabolism..Lipid.Metabolism..Fatty.acid.metabolism | -0.0946530 | 0.0230876 | -0.1399040 | -0.0494021 | -4.099727 | 0.0000414 | 0.0030885 |
| Metabolism..Carbohydrate.Metabolism..Pentose.and.glucuronate.interconversions | 0.0660993 | 0.0182315 | 0.0303662 | 0.1018323 | 3.625554 | 0.0002883 | 0.0161472 |
| Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.ansamycins | 0.0880979 | 0.0249367 | 0.0392229 | 0.1369729 | 3.532866 | 0.0004111 | 0.0184164 |
| Metabolism..Carbohydrate.Metabolism..Pentose.phosphate.pathway | 0.0397027 | 0.0119850 | 0.0162125 | 0.0631929 | 3.312691 | 0.0009240 | 0.0301535 |
| Organismal.Systems..Environmental.Adaptation..Plant.pathogen.interaction | 0.0459717 | 0.0139004 | 0.0187273 | 0.0732161 | 3.307211 | 0.0009423 | 0.0301535 |
| Metabolism..Lipid.Metabolism..Fatty.acid.biosynthesis | 0.0443805 | 0.0142821 | 0.0163880 | 0.0723729 | 3.107413 | 0.0018873 | 0.0528452 |
| Organismal.Systems..Endocrine.System..Adipocytokine.signaling.pathway | -0.1279232 | 0.0422790 | -0.2107885 | -0.0450579 | -3.025693 | 0.0024806 | 0.0617403 |
| Genetic.Information.Processing..Translation..Ribosome.biogenesis.in.eukaryotes | -0.0721010 | 0.0246330 | -0.1203808 | -0.0238213 | -2.927011 | 0.0034224 | 0.0766609 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Vitamin.B6.metabolism | -0.0291619 | 0.0100993 | -0.0489563 | -0.0093676 | -2.887504 | 0.0038831 | 0.0790743 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Porphyrin.and.chlorophyll.metabolism | 0.0626417 | 0.0223883 | 0.0187615 | 0.1065219 | 2.797970 | 0.0051425 | 0.0959931 |
| Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms | 0.0364030 | 0.0131564 | 0.0106170 | 0.0621889 | 2.766949 | 0.0056584 | 0.0974979 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…other.enzymes | 0.0353067 | 0.0131936 | 0.0094477 | 0.0611657 | 2.676042 | 0.0074497 | 0.1191955 |
| Genetic.Information.Processing..Replication.and.Repair..Base.excision.repair | 0.0148692 | 0.0057662 | 0.0035677 | 0.0261706 | 2.578692 | 0.0099175 | 0.1481015 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Biotin.metabolism | 0.0443338 | 0.0180359 | 0.0089842 | 0.0796835 | 2.458094 | 0.0139677 | 0.1955472 |
| Unclassified..Cellular.Processes.and.Signaling..Inorganic.ion.transport.and.metabolism | -0.0854735 | 0.0363810 | -0.1567790 | -0.0141680 | -2.349397 | 0.0188039 | 0.2441364 |
| Metabolism..Lipid.Metabolism..Glycerolipid.metabolism | 0.0381790 | 0.0163608 | 0.0061125 | 0.0702456 | 2.333573 | 0.0196181 | 0.2441364 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Toluene.degradation | -0.0789239 | 0.0346447 | -0.1468262 | -0.0110215 | -2.278094 | 0.0227210 | 0.2625265 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…cytochrome.P450 | -0.1077738 | 0.0475573 | -0.2009843 | -0.0145632 | -2.266188 | 0.0234399 | 0.2625265 |
| Organismal.Systems..Endocrine.System..Insulin.signaling.pathway | 0.0604418 | 0.0275299 | 0.0064843 | 0.1143993 | 2.195500 | 0.0281278 | 0.2934589 |
| Unclassified..Genetic.Information.Processing..Protein.folding.and.associated.processing | -0.0110271 | 0.0050446 | -0.0209143 | -0.0011398 | -2.185914 | 0.0288219 | 0.2934589 |
| Metabolism..Metabolism.of.Other.Amino.Acids..D.Alanine.metabolism | 0.0214157 | 0.0100361 | 0.0017453 | 0.0410862 | 2.133866 | 0.0328537 | 0.3199669 |
| Metabolism..Amino.Acid.Metabolism..Glycine..serine.and.threonine.metabolism | -0.0151926 | 0.0072327 | -0.0293685 | -0.0010168 | -2.100542 | 0.0356812 | 0.3248701 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Nicotinate.and.nicotinamide.metabolism | -0.0326563 | 0.0156489 | -0.0633275 | -0.0019850 | -2.086812 | 0.0369051 | 0.3248701 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Metabolism.of.xenobiotics.by.cytochrome.P450 | -0.0993704 | 0.0480715 | -0.1935887 | -0.0051520 | -2.067137 | 0.0387213 | 0.3248701 |
| Genetic.Information.Processing..Translation..RNA.transport | 0.0621342 | 0.0301254 | 0.0030895 | 0.1211788 | 2.062518 | 0.0391584 | 0.3248701 |
| Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Tetracycline.biosynthesis | 0.0562197 | 0.0281702 | 0.0010072 | 0.1114323 | 1.995717 | 0.0459647 | 0.3514947 |
| Genetic.Information.Processing..Folding..Sorting.and.Degradation..Proteasome | -0.0963819 | 0.0484326 | -0.1913081 | -0.0014557 | -1.990020 | 0.0465887 | 0.3514947 |
| Human.Diseases..Infectious.Diseases..Epithelial.cell.signaling.in.Helicobacter.pylori.infection | 0.0458477 | 0.0231344 | 0.0005050 | 0.0911903 | 1.981795 | 0.0475022 | 0.3514947 |
| Metabolism..Carbohydrate.Metabolism..Amino.sugar.and.nucleotide.sugar.metabolism | 0.0282869 | 0.0143465 | 0.0001683 | 0.0564055 | 1.971696 | 0.0486444 | 0.3514947 |
metadat<-metatab.show(metatab=pathmetatab.zi.noha$random,taxacom.pooled.tab=pathcom.zi.noha,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=6,forest.axis.text.x=6)
Level 2
kable(metatab.show(metatab=pathmetatab.zi.nohav$random,taxacom.pooled.tab=pathcom.zi.nohav,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Metabolism..Glycan.Biosynthesis.and.Metabolism | 0.0519612 | 0.0115735 | 0.0292776 | 0.0746448 | 4.489673 | 0.0000071 | 0.0002639 |
| Genetic.Information.Processing..Transcription | 0.0140217 | 0.0051204 | 0.0039858 | 0.0240575 | 2.738388 | 0.0061741 | 0.1142213 |
| Environmental.Information.Processing..Signaling.Molecules.and.Interaction | -0.0463997 | 0.0186814 | -0.0830146 | -0.0097848 | -2.483737 | 0.0130012 | 0.1603479 |
metadat<-metatab.show(metatab=pathmetatab.zi.nohav$random,taxacom.pooled.tab=pathcom.zi.nohav,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=7,forest.axis.text.x=7)
Level 3
kable(metatab.show(metatab=pathmetatab.zi.nohav$random,taxacom.pooled.tab=pathcom.zi.nohav,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Metabolism..Carbohydrate.Metabolism..Fructose.and.mannose.metabolism | 0.0695846 | 0.0140831 | 0.0419821 | 0.0971870 | 4.940987 | 0.0000008 | 0.0001757 |
| Cellular.Processes..Transport.and.Catabolism..Peroxisome | -0.0593738 | 0.0158836 | -0.0905051 | -0.0282425 | -3.738054 | 0.0001854 | 0.0209558 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Xylene.degradation | -0.0564881 | 0.0187429 | -0.0932235 | -0.0197527 | -3.013838 | 0.0025797 | 0.1577341 |
| Metabolism..Lipid.Metabolism..Fatty.acid.metabolism | -0.0893386 | 0.0299073 | -0.1479559 | -0.0307213 | -2.987181 | 0.0028156 | 0.1577341 |
| Genetic.Information.Processing..Folding..Sorting.and.Degradation..Chaperones.and.folding.catalysts | 0.0242035 | 0.0083216 | 0.0078934 | 0.0405136 | 2.908510 | 0.0036316 | 0.1577341 |
| Genetic.Information.Processing..Translation..Ribosome.biogenesis.in.eukaryotes | -0.0711084 | 0.0249038 | -0.1199190 | -0.0222978 | -2.855322 | 0.0042993 | 0.1577341 |
| Metabolism..Carbohydrate.Metabolism..Pentose.and.glucuronate.interconversions | 0.0503206 | 0.0179765 | 0.0150873 | 0.0855539 | 2.799240 | 0.0051223 | 0.1577341 |
| Metabolism..Lipid.Metabolism..Fatty.acid.biosynthesis | 0.0451811 | 0.0163033 | 0.0132272 | 0.0771349 | 2.771288 | 0.0055835 | 0.1577341 |
| Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.ansamycins | 0.0717378 | 0.0274480 | 0.0179408 | 0.1255348 | 2.613593 | 0.0089596 | 0.2249850 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Vitamin.B6.metabolism | -0.0243667 | 0.0095096 | -0.0430052 | -0.0057281 | -2.562315 | 0.0103977 | 0.2302045 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Biotin.metabolism | 0.0480086 | 0.0190681 | 0.0106359 | 0.0853814 | 2.517749 | 0.0118107 | 0.2302045 |
| Metabolism..Carbohydrate.Metabolism..Pentose.phosphate.pathway | 0.0334309 | 0.0133423 | 0.0072805 | 0.0595814 | 2.505633 | 0.0122232 | 0.2302045 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…other.enzymes | 0.0381120 | 0.0159839 | 0.0067842 | 0.0694399 | 2.384403 | 0.0171069 | 0.2973962 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Chloroalkane.and.chloroalkene.degradation | -0.0440491 | 0.0187390 | -0.0807769 | -0.0073214 | -2.350670 | 0.0187396 | 0.3025111 |
| Genetic.Information.Processing..Replication.and.Repair..Base.excision.repair | 0.0136657 | 0.0059388 | 0.0020258 | 0.0253056 | 2.301070 | 0.0213877 | 0.3222407 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…cytochrome.P450 | -0.1047320 | 0.0486254 | -0.2000359 | -0.0094280 | -2.153855 | 0.0312515 | 0.4116602 |
| Environmental.Information.Processing..Signal.Transduction..Phosphatidylinositol.signaling.system | 0.0181668 | 0.0084431 | 0.0016186 | 0.0347150 | 2.151673 | 0.0314231 | 0.4116602 |
| Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms | 0.0344968 | 0.0161602 | 0.0028235 | 0.0661702 | 2.134681 | 0.0327871 | 0.4116602 |
| Metabolism..Metabolism.of.Other.Amino.Acids..D.Alanine.metabolism | 0.0224208 | 0.0107122 | 0.0014253 | 0.0434164 | 2.093014 | 0.0363479 | 0.4323489 |
| Genetic.Information.Processing..Folding..Sorting.and.Degradation..Proteasome | -0.1014237 | 0.0500534 | -0.1995265 | -0.0033209 | -2.026311 | 0.0427329 | 0.4828817 |
metadat<-metatab.show(metatab=pathmetatab.zi.nohav$random,taxacom.pooled.tab=pathcom.zi.nohav,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=6,forest.axis.text.x=6)
Level 2
kable(metatab.show(metatab=pathmetatab.zi.nounc$random,taxacom.pooled.tab=pathcom.zi.nounc,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Environmental.Information.Processing..Signaling.Molecules.and.Interaction | -0.0481241 | 0.0174642 | -0.0823533 | -0.0138949 | -2.755588 | 0.0058587 | 0.2167706 |
| Genetic.Information.Processing..Transcription | 0.0159785 | 0.0066440 | 0.0029565 | 0.0290005 | 2.404959 | 0.0161743 | 0.2992240 |
metadat<-metatab.show(metatab=pathmetatab.zi.nounc$random,taxacom.pooled.tab=pathcom.zi.nounc,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=7,forest.axis.text.x=7)
Level 3
kable(metatab.show(metatab=pathmetatab.zi.nounc$random,taxacom.pooled.tab=pathcom.zi.nounc,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Metabolism..Carbohydrate.Metabolism..Fructose.and.mannose.metabolism | 0.0748047 | 0.0153469 | 0.0447254 | 0.1048841 | 4.874259 | 0.0000011 | 0.0002468 |
| Cellular.Processes..Transport.and.Catabolism..Peroxisome | -0.0634911 | 0.0150010 | -0.0928925 | -0.0340896 | -4.232451 | 0.0000231 | 0.0026121 |
| Metabolism..Lipid.Metabolism..Fatty.acid.metabolism | -0.0862183 | 0.0256901 | -0.1365700 | -0.0358667 | -3.356095 | 0.0007905 | 0.0595520 |
| Genetic.Information.Processing..Replication.and.Repair..Base.excision.repair | 0.0170264 | 0.0054765 | 0.0062925 | 0.0277602 | 3.108958 | 0.0018775 | 0.0874960 |
| Metabolism..Carbohydrate.Metabolism..Pentose.and.glucuronate.interconversions | 0.0618250 | 0.0201180 | 0.0223944 | 0.1012557 | 3.073116 | 0.0021184 | 0.0874960 |
| Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.ansamycins | 0.0758176 | 0.0248949 | 0.0270245 | 0.1246108 | 3.045504 | 0.0023229 | 0.0874960 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Vitamin.B6.metabolism | -0.0286169 | 0.0096273 | -0.0474861 | -0.0097477 | -2.972464 | 0.0029542 | 0.0953784 |
| Metabolism..Lipid.Metabolism..Fatty.acid.biosynthesis | 0.0415976 | 0.0143916 | 0.0133905 | 0.0698047 | 2.890399 | 0.0038475 | 0.1004504 |
| Genetic.Information.Processing..Translation..Ribosome.biogenesis.in.eukaryotes | -0.0707846 | 0.0245939 | -0.1189877 | -0.0225816 | -2.878143 | 0.0040002 | 0.1004504 |
| Metabolism..Carbohydrate.Metabolism..Pentose.phosphate.pathway | 0.0339486 | 0.0122911 | 0.0098585 | 0.0580387 | 2.762045 | 0.0057441 | 0.1298157 |
| Metabolism..Metabolism.of.Other.Amino.Acids..D.Alanine.metabolism | 0.0230465 | 0.0101136 | 0.0032242 | 0.0428689 | 2.278763 | 0.0226811 | 0.4659945 |
| Unclassified..Genetic.Information.Processing..Protein.folding.and.associated.processing | -0.0110941 | 0.0050162 | -0.0209256 | -0.0012625 | -2.211653 | 0.0269906 | 0.5077651 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…other.enzymes | 0.0306056 | 0.0142804 | 0.0026166 | 0.0585946 | 2.143191 | 0.0320978 | 0.5077651 |
| Organismal.Systems..Endocrine.System..Adipocytokine.signaling.pathway | -0.1040949 | 0.0493909 | -0.2008994 | -0.0072905 | -2.107573 | 0.0350680 | 0.5077651 |
| Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms | 0.0263807 | 0.0125598 | 0.0017639 | 0.0509975 | 2.100403 | 0.0356934 | 0.5077651 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…cytochrome.P450 | -0.0998941 | 0.0476250 | -0.1932373 | -0.0065509 | -2.097515 | 0.0359480 | 0.5077651 |
| Human.Diseases..Infectious.Diseases..Epithelial.cell.signaling.in.Helicobacter.pylori.infection | 0.0476778 | 0.0233794 | 0.0018550 | 0.0935006 | 2.039309 | 0.0414192 | 0.5377872 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Nicotinate.and.nicotinamide.metabolism | -0.0310355 | 0.0154611 | -0.0613386 | -0.0007324 | -2.007333 | 0.0447142 | 0.5377872 |
| Metabolism..Carbohydrate.Metabolism..Amino.sugar.and.nucleotide.sugar.metabolism | 0.0289152 | 0.0146735 | 0.0001557 | 0.0576747 | 1.970575 | 0.0487726 | 0.5377872 |
metadat<-metatab.show(metatab=pathmetatab.zi.nounc$random,taxacom.pooled.tab=pathcom.zi.nounc,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=6,forest.axis.text.x=6)
load(paste(dir,"data/pathmetatab.zi.vagcs.rda",sep=""))
Level 2
kable(metatab.show(metatab=pathmetatab.zi.vag$random,taxacom.pooled.tab=pathcom.zi.vag,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Human.Diseases..Infectious.Diseases | -0.0484981 | 0.0139289 | -0.0757982 | -0.0211980 | -3.481839 | 0.0004980 | 0.0189234 |
| Human.Diseases..Neurodegenerative.Diseases | -0.0840083 | 0.0286090 | -0.1400809 | -0.0279357 | -2.936430 | 0.0033201 | 0.0630826 |
| Unclassified..Genetic.Information.Processing | -0.0202899 | 0.0083563 | -0.0366680 | -0.0039118 | -2.428091 | 0.0151785 | 0.1607306 |
| Environmental.Information.Processing..Signal.Transduction | -0.0553524 | 0.0231749 | -0.1007744 | -0.0099304 | -2.388463 | 0.0169190 | 0.1607306 |
| Metabolism..Metabolism.of.Other.Amino.Acids | -0.0167393 | 0.0076171 | -0.0316685 | -0.0018101 | -2.197602 | 0.0279775 | 0.2126291 |
metadat<-metatab.show(metatab=pathmetatab.zi.vag$random,taxacom.pooled.tab=pathcom.zi.vag,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=7,forest.axis.text.x=7)
Level 3 All
metadat<-metatab.show(metatab=pathmetatab.zi.vag$random,taxacom.pooled.tab=pathcom.zi.vag,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=4,forest.axis.text.x=4)
Significant only (pooled p<0.05)
kable(metatab.show(metatab=pathmetatab.zi.vag$random,taxacom.pooled.tab=pathcom.zi.vag,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Metabolism..Carbohydrate.Metabolism..Pentose.phosphate.pathway | 0.0461974 | 0.0092376 | 0.0280921 | 0.0643027 | 5.001030 | 0.0000006 | 0.0001306 |
| Metabolism..Carbohydrate.Metabolism..Propanoate.metabolism | -0.0570404 | 0.0134718 | -0.0834447 | -0.0306362 | -4.234055 | 0.0000230 | 0.0026279 |
| Metabolism..Lipid.Metabolism..Fatty.acid.metabolism | -0.0993213 | 0.0284718 | -0.1551250 | -0.0435176 | -3.488412 | 0.0004859 | 0.0370903 |
| Metabolism..Carbohydrate.Metabolism..Fructose.and.mannose.metabolism | 0.0720518 | 0.0219622 | 0.0290068 | 0.1150969 | 3.280725 | 0.0010354 | 0.0592770 |
| Unclassified..Cellular.Processes.and.Signaling..Sporulation | 0.2691131 | 0.0880246 | 0.0965881 | 0.4416381 | 3.057250 | 0.0022338 | 0.0994375 |
| Metabolism..Carbohydrate.Metabolism..Amino.sugar.and.nucleotide.sugar.metabolism | 0.0398818 | 0.0132461 | 0.0139199 | 0.0658437 | 3.010830 | 0.0026054 | 0.0994375 |
| Metabolism..Enzyme.Families..Peptidases | 0.0213727 | 0.0072551 | 0.0071530 | 0.0355924 | 2.945901 | 0.0032202 | 0.1053451 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Pantothenate.and.CoA.biosynthesis | 0.0229267 | 0.0086275 | 0.0060172 | 0.0398362 | 2.657414 | 0.0078743 | 0.2154792 |
| Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms | 0.0343939 | 0.0132238 | 0.0084757 | 0.0603121 | 2.600904 | 0.0092979 | 0.2154792 |
| Metabolism..Carbohydrate.Metabolism..Butanoate.metabolism | -0.0390622 | 0.0152890 | -0.0690281 | -0.0090962 | -2.554916 | 0.0106214 | 0.2154792 |
| Metabolism..Metabolism.of.Other.Amino.Acids..Glutathione.metabolism | -0.0759232 | 0.0298932 | -0.1345127 | -0.0173336 | -2.539817 | 0.0110911 | 0.2154792 |
| Metabolism..Amino.Acid.Metabolism..Tryptophan.metabolism | -0.1037075 | 0.0410760 | -0.1842150 | -0.0232001 | -2.524773 | 0.0115773 | 0.2154792 |
| Metabolism..Amino.Acid.Metabolism..Lysine.degradation | -0.0938693 | 0.0377849 | -0.1679263 | -0.0198124 | -2.484310 | 0.0129803 | 0.2154792 |
| Environmental.Information.Processing..Membrane.Transport..Bacterial.secretion.system | -0.0478536 | 0.0193032 | -0.0856872 | -0.0100200 | -2.479047 | 0.0131734 | 0.2154792 |
| Unclassified..Genetic.Information.Processing..Replication..recombination.and.repair.proteins | -0.0443230 | 0.0181120 | -0.0798220 | -0.0088241 | -2.447159 | 0.0143987 | 0.2198206 |
| Environmental.Information.Processing..Signal.Transduction..Two.component.system | -0.0610081 | 0.0262962 | -0.1125477 | -0.0094685 | -2.320034 | 0.0203390 | 0.2911026 |
| Metabolism..Carbohydrate.Metabolism..Pentose.and.glucuronate.interconversions | 0.0597831 | 0.0266534 | 0.0075434 | 0.1120228 | 2.242983 | 0.0248979 | 0.3300089 |
| Metabolism..Lipid.Metabolism..Sphingolipid.metabolism | 0.0973295 | 0.0437020 | 0.0116751 | 0.1829839 | 2.227115 | 0.0259396 | 0.3300089 |
| Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Limonene.and.pinene.degradation | -0.0813978 | 0.0386717 | -0.1571928 | -0.0056027 | -2.104844 | 0.0353049 | 0.4255172 |
| Metabolism..Amino.Acid.Metabolism..Valine..leucine.and.isoleucine.degradation | -0.0895058 | 0.0434446 | -0.1746557 | -0.0043559 | -2.060227 | 0.0393768 | 0.4326503 |
| Metabolism..Energy.Metabolism..Methane.metabolism | 0.0355941 | 0.0173608 | 0.0015675 | 0.0696207 | 2.050251 | 0.0403400 | 0.4326503 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Vitamin.B6.metabolism | -0.0328569 | 0.0162180 | -0.0646435 | -0.0010703 | -2.025958 | 0.0427691 | 0.4326503 |
| Organismal.Systems..Endocrine.System..Insulin.signaling.pathway | 0.0703689 | 0.0351516 | 0.0014731 | 0.1392648 | 2.001871 | 0.0452986 | 0.4326503 |
| Environmental.Information.Processing..Membrane.Transport..Secretion.system | -0.0508999 | 0.0255435 | -0.1009642 | -0.0008357 | -1.992679 | 0.0462966 | 0.4326503 |
| Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.ansamycins | 0.0805279 | 0.0405844 | 0.0009840 | 0.1600719 | 1.984209 | 0.0472326 | 0.4326503 |
metadat<-metatab.show(metatab=pathmetatab.zi.vag$random,taxacom.pooled.tab=pathcom.zi.vag,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=5,forest.axis.text.x=6)
Multiple testing adjusted Significant only (adjusted pooled p<0.1)
metadat<-metatab.show(metatab=pathmetatab.zi.vag$random,taxacom.pooled.tab=pathcom.zi.vag,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p.adjust", p.cutoff=0.1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=6.5,forest.axis.text.x=6)
Level 2
kable(metatab.show(metatab=pathmetatab.zi.cs$random,taxacom.pooled.tab=pathcom.zi.cs,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Human.Diseases..Infectious.Diseases | -0.1126764 | 0.0258973 | -0.1634341 | -0.0619188 | -4.350903 | 0.0000136 | 0.0005152 |
| Environmental.Information.Processing..Signal.Transduction | -0.1416460 | 0.0372818 | -0.2147171 | -0.0685750 | -3.799334 | 0.0001451 | 0.0027566 |
| Unclassified..Poorly.Characterized | -0.1803121 | 0.0692044 | -0.3159502 | -0.0446739 | -2.605500 | 0.0091740 | 0.0868309 |
| Metabolism..Energy.Metabolism | 0.0374528 | 0.0147283 | 0.0085859 | 0.0663197 | 2.542920 | 0.0109930 | 0.0868309 |
| Human.Diseases..Neurodegenerative.Diseases | -0.1436055 | 0.0567741 | -0.2548806 | -0.0323303 | -2.529420 | 0.0114251 | 0.0868309 |
| Genetic.Information.Processing..Replication.and.Repair | 0.1053877 | 0.0472699 | 0.0127404 | 0.1980350 | 2.229488 | 0.0257815 | 0.1311394 |
| Metabolism..Nucleotide.Metabolism | 0.1138804 | 0.0510945 | 0.0137370 | 0.2140238 | 2.228820 | 0.0258259 | 0.1311394 |
| Metabolism..Amino.Acid.Metabolism | 0.0256341 | 0.0117885 | 0.0025291 | 0.0487391 | 2.174502 | 0.0296674 | 0.1311394 |
| Unclassified..Cellular.Processes.and.Signaling | -0.1513433 | 0.0711795 | -0.2908525 | -0.0118341 | -2.126222 | 0.0334848 | 0.1311394 |
| Metabolism..Carbohydrate.Metabolism | 0.0257629 | 0.0121865 | 0.0018779 | 0.0496480 | 2.114057 | 0.0345104 | 0.1311394 |
metadat<-metatab.show(metatab=pathmetatab.zi.cs$random,taxacom.pooled.tab=pathcom.zi.cs,sumvar="path",tax.lev="l2",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=7,forest.axis.text.x=7)
Level 3 All
metadat<-metatab.show(metatab=pathmetatab.zi.cs$random,taxacom.pooled.tab=pathcom.zi.cs,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=4,forest.axis.text.y=4,forest.axis.text.x=4)
Significant only (pooled p<0.05)
kable(metatab.show(metatab=pathmetatab.zi.cs$random,taxacom.pooled.tab=pathcom.zi.cs,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="table"))
| estimate.nebf | se.nebf | ll.nebf | ul.nebf | z.nebf | p.nebf | p.adjust.nebf | |
|---|---|---|---|---|---|---|---|
| Metabolism..Amino.Acid.Metabolism..Lysine.degradation | -0.2410428 | 0.0589751 | -0.3566318 | -0.1254538 | -4.087199 | 0.0000437 | 0.0052013 |
| Environmental.Information.Processing..Membrane.Transport..Secretion.system | -0.1668053 | 0.0409038 | -0.2469752 | -0.0866354 | -4.077994 | 0.0000454 | 0.0052013 |
| Cellular.Processes..Cell.Motility..Cytoskeleton.proteins | 0.1946407 | 0.0501812 | 0.0962873 | 0.2929941 | 3.878754 | 0.0001050 | 0.0080144 |
| Environmental.Information.Processing..Signal.Transduction..Two.component.system | -0.1563058 | 0.0417345 | -0.2381040 | -0.0745076 | -3.745237 | 0.0001802 | 0.0103178 |
| Metabolism..Amino.Acid.Metabolism..Lysine.biosynthesis | 0.0932496 | 0.0261242 | 0.0420471 | 0.1444521 | 3.569470 | 0.0003577 | 0.0147737 |
| Metabolism..Amino.Acid.Metabolism..Tryptophan.metabolism | -0.2173867 | 0.0612575 | -0.3374491 | -0.0973242 | -3.548736 | 0.0003871 | 0.0147737 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Thiamine.metabolism | 0.0778945 | 0.0227453 | 0.0333146 | 0.1224745 | 3.424647 | 0.0006156 | 0.0201389 |
| Metabolism..Lipid.Metabolism..Biosynthesis.of.unsaturated.fatty.acids | -0.1598449 | 0.0480023 | -0.2539276 | -0.0657621 | -3.329941 | 0.0008686 | 0.0248649 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…other.enzymes | 0.0699567 | 0.0212525 | 0.0283025 | 0.1116109 | 3.291685 | 0.0009959 | 0.0253399 |
| Metabolism..Lipid.Metabolism..Fatty.acid.metabolism | -0.1490154 | 0.0458870 | -0.2389524 | -0.0590784 | -3.247440 | 0.0011645 | 0.0266667 |
| Genetic.Information.Processing..Folding..Sorting.and.Degradation..Sulfur.relay.system | -0.1371891 | 0.0433387 | -0.2221315 | -0.0522467 | -3.165507 | 0.0015481 | 0.0299670 |
| Metabolism..Carbohydrate.Metabolism..Galactose.metabolism | 0.1151450 | 0.0364226 | 0.0437581 | 0.1865320 | 3.161364 | 0.0015703 | 0.0299670 |
| Genetic.Information.Processing..Replication.and.Repair..Mismatch.repair | 0.0832394 | 0.0269024 | 0.0305117 | 0.1359671 | 3.094126 | 0.0019739 | 0.0347716 |
| Metabolism..Enzyme.Families..Peptidases | 0.0367908 | 0.0120243 | 0.0132236 | 0.0603581 | 3.059696 | 0.0022156 | 0.0362412 |
| Unclassified..Cellular.Processes.and.Signaling..Inorganic.ion.transport.and.metabolism | -0.2280142 | 0.0753721 | -0.3757408 | -0.0802875 | -3.025179 | 0.0024849 | 0.0378887 |
| Metabolism..Biosynthesis.of.Other.Secondary.Metabolites..Phenylpropanoid.biosynthesis | 0.1624628 | 0.0543283 | 0.0559814 | 0.2689443 | 2.990393 | 0.0027862 | 0.0378887 |
| Genetic.Information.Processing..Replication.and.Repair..Nucleotide.excision.repair | 0.1893190 | 0.0633790 | 0.0650986 | 0.3135395 | 2.987096 | 0.0028164 | 0.0378887 |
| Metabolism..Carbohydrate.Metabolism..Amino.sugar.and.nucleotide.sugar.metabolism | 0.0854708 | 0.0289330 | 0.0287632 | 0.1421784 | 2.954096 | 0.0031359 | 0.0378887 |
| Genetic.Information.Processing..Replication.and.Repair..DNA.replication.proteins | 0.0714555 | 0.0241948 | 0.0240344 | 0.1188765 | 2.953335 | 0.0031436 | 0.0378887 |
| Unclassified..Cellular.Processes.and.Signaling..Sporulation | 0.4258710 | 0.1509691 | 0.1299770 | 0.7217649 | 2.820915 | 0.0047887 | 0.0548305 |
| Unclassified..Cellular.Processes.and.Signaling..Other.ion.coupled.transporters | -0.1077009 | 0.0387677 | -0.1836842 | -0.0317177 | -2.778113 | 0.0054676 | 0.0596224 |
| Metabolism..Amino.Acid.Metabolism..Valine..leucine.and.isoleucine.degradation | -0.1456712 | 0.0536771 | -0.2508764 | -0.0404661 | -2.713844 | 0.0066508 | 0.0666673 |
| Metabolism..Metabolism.of.Other.Amino.Acids..Glutathione.metabolism | -0.1200119 | 0.0442873 | -0.2068134 | -0.0332105 | -2.709852 | 0.0067313 | 0.0666673 |
| Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms | 0.0526806 | 0.0195297 | 0.0144032 | 0.0909581 | 2.697465 | 0.0069870 | 0.0666673 |
| Unclassified..Metabolism..Metabolism.of.cofactors.and.vitamins | -0.1333637 | 0.0498366 | -0.2310416 | -0.0356858 | -2.676021 | 0.0074502 | 0.0682438 |
| Human.Diseases..Infectious.Diseases..Vibrio.cholerae.pathogenic.cycle | -0.1267418 | 0.0476819 | -0.2201966 | -0.0332870 | -2.658069 | 0.0078590 | 0.0692195 |
| Metabolism..Nucleotide.Metabolism..Pyrimidine.metabolism | 0.1104510 | 0.0429456 | 0.0262793 | 0.1946228 | 2.571884 | 0.0101147 | 0.0815516 |
| Metabolism..Amino.Acid.Metabolism..Phenylalanine..tyrosine.and.tryptophan.biosynthesis | 0.0758791 | 0.0298059 | 0.0174605 | 0.1342977 | 2.545772 | 0.0109036 | 0.0815516 |
| Unclassified..Poorly.Characterized..Function.unknown | -0.1569012 | 0.0616904 | -0.2778123 | -0.0359902 | -2.543364 | 0.0109791 | 0.0815516 |
| Unclassified..Metabolism..Biosynthesis.and.biodegradation.of.secondary.metabolites | -0.2158734 | 0.0850259 | -0.3825210 | -0.0492257 | -2.538914 | 0.0111197 | 0.0815516 |
| Metabolism..Enzyme.Families..Protein.kinases | -0.1293255 | 0.0510231 | -0.2293288 | -0.0293222 | -2.534648 | 0.0112560 | 0.0815516 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..One.carbon.pool.by.folate | 0.1439633 | 0.0568953 | 0.0324506 | 0.2554761 | 2.530320 | 0.0113959 | 0.0815516 |
| Metabolism..Energy.Metabolism..Methane.metabolism | 0.0633891 | 0.0251859 | 0.0140256 | 0.1127526 | 2.516847 | 0.0118410 | 0.0821696 |
| Cellular.Processes..Cell.Growth.and.Death..Cell.cycle…Caulobacter | 0.2069000 | 0.0840125 | 0.0422384 | 0.3715616 | 2.462727 | 0.0137885 | 0.0928694 |
| Genetic.Information.Processing..Replication.and.Repair..DNA.replication | 0.0956593 | 0.0391391 | 0.0189480 | 0.1723706 | 2.444083 | 0.0145221 | 0.0950160 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Ubiquinone.and.other.terpenoid.quinone.biosynthesis | -0.1294977 | 0.0541391 | -0.2356083 | -0.0233871 | -2.391946 | 0.0167593 | 0.1066077 |
| Human.Diseases..Infectious.Diseases..Pertussis | -0.3459067 | 0.1463292 | -0.6327068 | -0.0591067 | -2.363893 | 0.0180840 | 0.1119254 |
| Metabolism..Carbohydrate.Metabolism..Starch.and.sucrose.metabolism | 0.0968519 | 0.0416485 | 0.0152222 | 0.1784815 | 2.325456 | 0.0200476 | 0.1186550 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Polycyclic.aromatic.hydrocarbon.degradation | 0.1728426 | 0.0745087 | 0.0268082 | 0.3188769 | 2.319764 | 0.0203536 | 0.1186550 |
| Metabolism..Metabolism.of.Cofactors.and.Vitamins..Pantothenate.and.CoA.biosynthesis | 0.0398297 | 0.0172203 | 0.0060784 | 0.0735810 | 2.312943 | 0.0207258 | 0.1186550 |
| Unclassified..Cellular.Processes.and.Signaling..Electron.transfer.carriers | -0.3152297 | 0.1370671 | -0.5838763 | -0.0465831 | -2.299820 | 0.0214584 | 0.1198530 |
| Cellular.Processes..Cell.Motility..Bacterial.motility.proteins | -0.1964298 | 0.0868495 | -0.3666517 | -0.0262078 | -2.261725 | 0.0237144 | 0.1292999 |
| Metabolism..Amino.Acid.Metabolism..Amino.acid.related.enzymes | 0.1029036 | 0.0459318 | 0.0128789 | 0.1929283 | 2.240355 | 0.0250679 | 0.1335010 |
| Metabolism..Glycan.Biosynthesis.and.Metabolism..Peptidoglycan.biosynthesis | 0.1655029 | 0.0742134 | 0.0200473 | 0.3109585 | 2.230095 | 0.0257412 | 0.1337390 |
| Metabolism..Glycan.Biosynthesis.and.Metabolism..Lipopolysaccharide.biosynthesis.proteins | -0.4913860 | 0.2211418 | -0.9248161 | -0.0579560 | -2.222040 | 0.0262806 | 0.1337390 |
| Unclassified..Cellular.Processes.and.Signaling..Membrane.and.intracellular.structural.molecules | -0.3249676 | 0.1470898 | -0.6132583 | -0.0366768 | -2.209314 | 0.0271528 | 0.1338000 |
| Metabolism..Energy.Metabolism..Photosynthesis | 0.3092523 | 0.1402569 | 0.0343539 | 0.5841507 | 2.204900 | 0.0274611 | 0.1338000 |
| Metabolism..Energy.Metabolism..Photosynthesis.proteins | 0.2924081 | 0.1367362 | 0.0244100 | 0.5604062 | 2.138483 | 0.0324776 | 0.1419698 |
| Genetic.Information.Processing..Replication.and.Repair..Homologous.recombination | 0.1521295 | 0.0711988 | 0.0125825 | 0.2916766 | 2.136688 | 0.0326234 | 0.1419698 |
| Genetic.Information.Processing..Folding..Sorting.and.Degradation..Protein.export | 0.2001823 | 0.0937140 | 0.0165063 | 0.3838583 | 2.136099 | 0.0326714 | 0.1419698 |
| Metabolism..Lipid.Metabolism..Sphingolipid.metabolism | 0.3160602 | 0.1483752 | 0.0252501 | 0.6068702 | 2.130141 | 0.0331600 | 0.1419698 |
| Genetic.Information.Processing..Translation..Translation.factors | 0.1979245 | 0.0932695 | 0.0151195 | 0.3807294 | 2.122070 | 0.0338319 | 0.1419698 |
| Genetic.Information.Processing..Replication.and.Repair..DNA.repair.and.recombination.proteins | 0.1416179 | 0.0668713 | 0.0105526 | 0.2726833 | 2.117768 | 0.0341947 | 0.1419698 |
| Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Prenyltransferases | 0.0689059 | 0.0325922 | 0.0050264 | 0.1327855 | 2.114184 | 0.0344996 | 0.1419698 |
| Unclassified..Poorly.Characterized..General.function.prediction.only | -0.1479579 | 0.0699906 | -0.2851369 | -0.0107788 | -2.113968 | 0.0345180 | 0.1419698 |
| Unclassified..Cellular.Processes.and.Signaling..Signal.transduction.mechanisms | -0.1179107 | 0.0558385 | -0.2273522 | -0.0084693 | -2.111638 | 0.0347175 | 0.1419698 |
| Metabolism..Metabolism.of.Other.Amino.Acids..beta.Alanine.metabolism | -0.1460591 | 0.0714335 | -0.2860661 | -0.0060521 | -2.044687 | 0.0408857 | 0.1642602 |
| Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Limonene.and.pinene.degradation | -0.1832241 | 0.0900308 | -0.3596813 | -0.0067669 | -2.035126 | 0.0418382 | 0.1651887 |
| Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.siderophore.group.nonribosomal.peptides | -0.2787206 | 0.1381849 | -0.5495580 | -0.0078831 | -2.017012 | 0.0436943 | 0.1690090 |
| Genetic.Information.Processing..Translation..Ribosome | 0.2622067 | 0.1303595 | 0.0067069 | 0.5177065 | 2.011413 | 0.0442818 | 0.1690090 |
| Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Toluene.degradation | -0.0942549 | 0.0471773 | -0.1867207 | -0.0017891 | -1.997887 | 0.0457289 | 0.1716708 |
| Genetic.Information.Processing..Replication.and.Repair..Chromosome | 0.0281142 | 0.0141358 | 0.0004085 | 0.0558200 | 1.988861 | 0.0467165 | 0.1725498 |
| Metabolism..Carbohydrate.Metabolism..Glyoxylate.and.dicarboxylate.metabolism | -0.0952237 | 0.0482937 | -0.1898776 | -0.0005698 | -1.971762 | 0.0486368 | 0.1767908 |
metadat<-metatab.show(metatab=pathmetatab.zi.cs$random,taxacom.pooled.tab=pathcom.zi.cs,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p", p.cutoff=0.05,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=5,forest.axis.text.x=6)
Multiple testing adjusted Significant only (adjusted pooled p<0.1)
metadat<-metatab.show(metatab=pathmetatab.zi.cs$random,taxacom.pooled.tab=pathcom.zi.cs,sumvar="path",tax.lev="l3",showvar=".nebf",p.cutoff.type="p.adjust", p.cutoff=0.1,display="data",fill.value="log(OR)")
meta.niceplot(metadat=metadat,sumtype="path",leg.key.size=1,leg.text.size=8,heat.text.x.size=6,forest.axis.text.y=6.5,forest.axis.text.x=5)
All analyses are adjusted for age of infants or breastfeeding status at sample collection and accounting for repeated/longitudinal sample collection.
load(paste(dir,"data/taxacom.612plus.food5.exbf2f.rda",sep=""))
For Subramanian (Bangladesh) data only.
Change in taxa relative abundance in duration of exclusive bf >2months vs. <=2 months
p.exbf.l2<-taxa.mean.plot(tabmean=taxa.meansdn.exbf2.rm,taxlist=taxlist.rm,tax.lev="l2", comvar="month.exbf2", groupvar="age.sample",mean.filter=0.005)
p.exbf.l2$p
#dev.off()
GAMLSS
kable(taxcomtab.show(taxcomtab=taxacom.6plus.exbf2.zi.rm,tax.select=p.exbf.l2$taxuse.rm, tax.lev="l2",p.adjust.method="fdr"))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| firmicutes | -0.2451898 | 0.0633103 | -3.872828 | 0.0001188 | 0.0004750 | -0.3692779 | -0.1211016 |
| actinobacteria | 0.2267863 | 0.0709545 | 3.196223 | 0.0014620 | 0.0029239 | 0.0877156 | 0.3658570 |
p.exbf.l4<-taxa.mean.plot(tabmean=taxa.meansdn.exbf2.rm,taxlist=taxlist.rm,tax.lev="l4", comvar="month.exbf2", groupvar="age.sample",mean.filter=0.005)
p.exbf.l4$p
GAMLSS
kable(taxcomtab.show(taxcomtab=taxacom.6plus.exbf2.zi.rm,tax.select=p.exbf.l4$taxuse.rm, tax.lev="l4",p.adjust.method="fdr"))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| actinobacteria.c__coriobacteriia.o__coriobacteriales | -0.2485089 | 0.0652555 | -3.808244 | 0.0001536 | 0.0007304 | -0.3764097 | -0.1206081 |
| firmicutes.c__bacilli.o__lactobacillales | -0.2705907 | 0.0725448 | -3.729979 | 0.0002087 | 0.0007304 | -0.4127786 | -0.1284028 |
| actinobacteria.c__actinobacteria.o__bifidobacteriales | 0.2543014 | 0.0712188 | 3.570704 | 0.0003831 | 0.0008938 | 0.1147125 | 0.3938904 |
| firmicutes.c__erysipelotrichi.o__erysipelotrichales | -0.1523709 | 0.0757899 | -2.010438 | 0.0448092 | 0.0784160 | -0.3009192 | -0.0038227 |
p.exbf.l5<-taxa.mean.plot(tabmean=taxa.meansdn.exbf2.rm,taxlist=taxlist.rm,tax.lev="l5", comvar="month.exbf2", groupvar="age.sample",mean.filter=0.005,show.taxname="short",legend.position="right")
p.exbf.l5$p
# better plot view
for (i in 1: length(taxa.meansdn.exbf2.rm)){
taxa.meansdn.exbf2.rm[[i]]$month.exbf2l<-mapvalues(taxa.meansdn.exbf2.rm[[i]]$month.exbf2,from=c("<=2 months",">2 months"),to=c("Duration EBF <=2 months","Duration EBF >2 months"))
}
p.exbf.l5.noleg<-taxa.mean.plot(tabmean=taxa.meansdn.exbf2.rm,taxlist=taxlist.rm,tax.lev="l5", comvar="month.exbf2l", groupvar="age.sample",mean.filter=0.005,legend.position="none")
grid.arrange(pexbf2,p.exbf.l5.noleg$p,nrow=1)
GAMLSS
kable(taxcomtab.show(taxcomtab=taxacom.6plus.exbf2.zi.rm,tax.select=p.exbf.l5$taxuse.rm, tax.lev="l5",p.adjust.method="fdr"))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| firmicutes.c__bacilli.o__lactobacillales.f__lactobacillaceae | -0.3106381 | 0.0757692 | -4.099792 | 0.0000467 | 0.0006074 | -0.4591458 | -0.1621304 |
| actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae | -0.2485089 | 0.0652555 | -3.808244 | 0.0001536 | 0.0009984 | -0.3764097 | -0.1206081 |
| actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae | 0.2543014 | 0.0712188 | 3.570704 | 0.0003831 | 0.0016600 | 0.1147125 | 0.3938904 |
| bacteroidetes.c__bacteroidia.o__bacteroidales.f__prevotellaceae | -0.2684295 | 0.0800336 | -3.353959 | 0.0008441 | 0.0027434 | -0.4252954 | -0.1115636 |
| firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae | -0.2173273 | 0.0799003 | -2.719980 | 0.0067053 | 0.0174337 | -0.3739319 | -0.0607226 |
| firmicutes.c__bacilli.o__lactobacillales.f__enterococcaceae | 0.1916596 | 0.0817407 | 2.344727 | 0.0193459 | 0.0419160 | 0.0314478 | 0.3518714 |
| firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae | -0.1523709 | 0.0757899 | -2.010438 | 0.0448092 | 0.0789058 | -0.3009192 | -0.0038227 |
| firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae | -0.1427715 | 0.0722439 | -1.976243 | 0.0485574 | 0.0789058 | -0.2843695 | -0.0011734 |
p.exbf.l6<-taxa.mean.plot(tabmean=taxa.meansdn.exbf2.rm,taxlist=taxlist.rm,tax.lev="l6", comvar="month.exbf2", groupvar="age.sample",mean.filter=0.005)
p.exbf.l6$p
GAMLSS
kable(taxcomtab.show(taxcomtab=taxacom.6plus.exbf2.zi.rm,tax.select=p.exbf.l6$taxuse.rm, tax.lev="l6",p.adjust.method="fdr"))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| firmicutes.c__bacilli.o__lactobacillales.f__lactobacillaceae.g__lactobacillus | -0.3315173 | 0.0755929 | -4.385561 | 0.0000135 | 0.0002844 | -0.4796795 | -0.1833552 |
| firmicutes.c__clostridia.oclostridiales.f.g__ | -0.3365252 | 0.0803138 | -4.190128 | 0.0000318 | 0.0003341 | -0.4939403 | -0.1791101 |
| actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae.g__bifidobacterium | 0.2542917 | 0.0712187 | 3.570578 | 0.0003833 | 0.0026828 | 0.1147032 | 0.3938803 |
| firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__.ruminococcus. | -0.2516784 | 0.0741715 | -3.393195 | 0.0007339 | 0.0035457 | -0.3970545 | -0.1063022 |
| bacteroidetes.c__bacteroidia.o__bacteroidales.f__prevotellaceae.g__prevotella | -0.2684274 | 0.0800336 | -3.353932 | 0.0008442 | 0.0035457 | -0.4252933 | -0.1115615 |
| actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__ | -0.2173260 | 0.0720564 | -3.016054 | 0.0026637 | 0.0093228 | -0.3585566 | -0.0760954 |
| firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae.g__ | -0.2429816 | 0.0847773 | -2.866116 | 0.0042923 | 0.0128768 | -0.4091452 | -0.0768181 |
| firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__blautia | -0.2191360 | 0.0791422 | -2.768889 | 0.0057874 | 0.0151919 | -0.3742548 | -0.0640173 |
| actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__collinsella | -0.1709286 | 0.0682455 | -2.504614 | 0.0125085 | 0.0291864 | -0.3046898 | -0.0371675 |
| firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__catenibacterium | -0.2297013 | 0.0955240 | -2.404645 | 0.0164716 | 0.0345904 | -0.4169284 | -0.0424743 |
| firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__ | -0.1613206 | 0.0722253 | -2.233574 | 0.0258594 | 0.0493679 | -0.3028823 | -0.0197590 |
| firmicutes.c__bacilli.o__lactobacillales.f__enterococcaceae.g__enterococcus | 0.1801273 | 0.0833351 | 2.161482 | 0.0310272 | 0.0542975 | 0.0167905 | 0.3434641 |
Subramania (Bangladesh) data only.
Only LME was used as GAMLSS has issues with small sample size (when stratifying). LME as showed above has lower power than GAMLSS in general.
load(paste(dir,"data/taxacom.dia.abf.rda",sep=""))
load(paste(dir,"data/taxacom.dia.exbf2.zinolong.rda",sep=""))
load(paste(dir,"data/taxacom.dia.bf.zinolong.rda",sep=""))
p.dia.exbf2.6plus.l5<-taxa.mean.plot(tabmean=taxa.meansdn.dia.exbf2.6plus.rm,taxlist=taxlist.rm,tax.lev="l5", comvar="diarrhea", groupvar="month.exbf2",mean.filter=0.005,legend.position="right")
p.dia.exbf2.6plus.l5$p
#more detail legend
teste<-taxa.meansdn.dia.exbf2.6plus.rm
for (i in 1:length(names(teste))){
teste[[i]]$month.exbf2l<-mapvalues(teste[[i]]$month.exbf2,from=c("<=2 months",">2 months"),to=c("Duration EBF <=2 months","Duration EBF >2 months"))
}
p.dia.exbf2.6plus.l5noleg<-taxa.mean.plot(tabmean=teste,taxlist=taxlist.rm,tax.lev="l5", comvar="diarrhea", groupvar="month.exbf2l",mean.filter=0.005,legend.position="none",ylab="Relative abundance (6 months - 2 years)")
In infants with duration of exclusive bf <=2 months
kable(taxcomtab.show(taxcomtab=taxacom.6plus.dia.exbf2.zi.rm, tax.lev="l5",tax.select=p.dia.exbf2.6plus.l5$taxuse.rm,p.adjust.method="fdr",p.cutoff=0.1))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae | -0.7714898 | 0.1993835 | -3.869376 | 0.0001287 | 0.0016732 | -1.1622815 | -0.3806981 |
| actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae | -0.6912195 | 0.1960537 | -3.525664 | 0.0004749 | 0.0030865 | -1.0754847 | -0.3069543 |
| firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae | 0.5260925 | 0.1836007 | 2.865417 | 0.0043999 | 0.0190660 | 0.1662351 | 0.8859498 |
In infants with duration of exclusive bf >2 months
kable(taxcomtab.show(taxcomtab=taxacom.6plus.dia.exbf2plus.zi.rm, tax.lev="l5",tax.select=p.dia.exbf2.6plus.l5$taxuse.rm,p.adjust.method="fdr",p.cutoff=0.1))
estimate se teststat pval pval.adjust ll ul ——— — ——— —– ———— — —
p.dia.exbf2.6plus.l6<-taxa.mean.plot(tabmean=taxa.meansdn.dia.exbf2.6plus.rm,taxlist=taxlist.rm,tax.lev="l6", comvar="diarrhea", groupvar="month.exbf2",mean.filter=0.005,legend.position = "right")
p.dia.exbf2.6plus.l6$p
GAMLSS
In infants with duration of exclusive bf <=2 months
kable(taxcomtab.show(taxcomtab=taxacom.6plus.dia.exbf2.zi.rm, tax.lev="l6",tax.select=p.dia.exbf2.6plus.l6$taxuse.rm,p.adjust.method="fdr",p.cutoff=0.1))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae.g__bifidobacterium | -0.7714809 | 0.1993838 | -3.869325 | 0.0001287 | 0.0027034 | -1.1622732 | -0.3806886 |
| firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae.g__streptococcus | 0.5243671 | 0.1841506 | 2.847491 | 0.0046498 | 0.0488224 | 0.1634320 | 0.8853023 |
| actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__collinsella | -0.4992648 | 0.2105904 | -2.370786 | 0.0182565 | 0.1237102 | -0.9120220 | -0.0865075 |
| actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__ | -0.4975598 | 0.2188542 | -2.273476 | 0.0235638 | 0.1237102 | -0.9265141 | -0.0686055 |
In infants with duration of exclusive bf >2 months
kable(taxcomtab.show(taxcomtab=taxacom.6plus.dia.exbf2plus.zi.rm, tax.lev="l6",tax.select=p.dia.exbf2.6plus.l6$taxuse.rm,p.adjust.method="fdr",p.cutoff=0.1))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__ | 0.3859128 | 0.2285308 | 1.688668 | 0.0923469 | 0.9809468 | -0.0620076 | 0.8338332 |
taxa.meansdn.dia.bf.6plus.rm.s<-list()
for (i in 1:5){
taxa.meansdn.dia.bf.6plus.rm.s[[i]]<-taxa.meansdn.dia.bf.6plus.rm[[i]][taxa.meansdn.dia.bf.6plus.rm[[i]]$bf!="ExclusiveBF",]
taxa.meansdn.dia.bf.6plus.rm.s[[i]]$bf<-drop.levels(taxa.meansdn.dia.bf.6plus.rm.s[[i]]$bf,reorder=FALSE)
}
names(taxa.meansdn.dia.bf.6plus.rm.s)<-names(taxa.meansdn.dia.bf.6plus.rm)
p.dia.bf.6plus.l5<-taxa.mean.plot(tabmean=taxa.meansdn.dia.bf.6plus.rm.s,tax.select=p.dia.exbf2.6plus.l5$taxuse.rm,taxlist=taxlist.rm,tax.lev="l5", comvar="diarrhea", groupvar="bf",mean.filter=0.005,show.taxname = "short")
p.dia.bf.6plus.l5$p
# reverse levels for better combined plot view
testr=taxa.meansdn.dia.bf.6plus.rm.s
for (i in 1: length(names(testr))){
testr[[i]]$bfl<-mapvalues(testr[[i]]$bf,from=c("No_BF","Non_exclusiveBF"),to=c("No BF when diarrhea","BF when diarrhea"))
testr[[i]]$bfl<-factor(testr[[i]]$bfl,levels=c("No BF when diarrhea","BF when diarrhea"))
}
p.dia.bf.6plus.l5.nolegend<-taxa.mean.plot(tabmean=testr,tax.select=p.dia.exbf2.6plus.l5$taxuse.rm,taxlist=taxlist.rm,tax.lev="l5", comvar="diarrhea", groupvar="bfl",mean.filter=0.005,show.taxname = "short",legend.position = "none",ylab="Relative abundance (6 months - 2 years)")
In non-exclusive bf infants
kable(taxcomtab.show(taxcomtab=taxacom.6plus.dia.nexbf.zi.rm, tax.lev="l5",tax.select=p.dia.bf.6plus.l5$taxuse.rm,p.adjust.method="fdr",p.cutoff=0.1))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae | -0.3440389 | 0.1461708 | -2.353678 | 0.0189040 | 0.2457518 | -0.6305337 | -0.0575442 |
| actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae | -0.2512789 | 0.1516653 | -1.656799 | 0.0980727 | 0.4727405 | -0.5485430 | 0.0459851 |
In no bf infants
kable(taxcomtab.show(taxcomtab=taxacom.6plus.dia.nobf.zi.rm, tax.lev="l5",tax.select=p.dia.bf.6plus.l5$taxuse.rm,p.adjust.method="fdr",p.cutoff=0.1))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae | 2.090855 | 0.6246379 | 3.347308 | 0.0017567 | 0.0228374 | 0.8665651 | 3.3151455 |
| actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae | -1.840426 | 0.7653211 | -2.404776 | 0.0207821 | 0.1350835 | -3.3404553 | -0.3403967 |
p.dia.bf.6plus.l6<-taxa.mean.plot(tabmean=taxa.meansdn.dia.bf.6plus.rm.s,tax.select=p.dia.exbf2.6plus.l6$taxuse.rm,taxlist=taxlist.rm,tax.lev="l6", comvar="diarrhea", groupvar="bf",mean.filter=0.005)
p.dia.bf.6plus.l6$p
In bf infants
kable(taxcomtab.show(taxcomtab=taxacom.6plus.dia.nexbf.zi.rm, tax.lev="l6",tax.select=p.dia.bf.6plus.l6$taxuse.rm,p.adjust.method="fdr",p.cutoff=0.1))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__collinsella | -0.2910103 | 0.1480753 | -1.965286 | 0.0498330 | 0.654706 | -0.5812379 | -0.0007827 |
| actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae.g__bifidobacterium | -0.2512877 | 0.1516657 | -1.656853 | 0.0980617 | 0.654706 | -0.5485524 | 0.0459770 |
In non-bf infants
kable(taxcomtab.show(taxcomtab=taxacom.6plus.dia.nobf.zi.rm, tax.lev="l6",tax.select=p.dia.bf.6plus.l6$taxuse.rm,p.adjust.method="fdr",p.cutoff=0.1))
| estimate | se | teststat | pval | pval.adjust | ll | ul | |
|---|---|---|---|---|---|---|---|
| firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae.g__streptococcus | 2.085766 | 0.6249206 | 3.337650 | 0.0018053 | 0.0379113 | 0.8609218 | 3.3106107 |
| actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae.g__bifidobacterium | -1.840411 | 0.7653209 | -2.404758 | 0.0207830 | 0.2182216 | -3.3404403 | -0.3403824 |
With GAMM fit and 95%CI.
rmdat.rm$dia.exbf2<-paste(rmdat.rm$diarrhea,rmdat.rm$month.exbf2,sep=".")
rmdat.rm<-as.data.frame(rmdat.rm)
rmdat.rm$dia.exbf2<-as.factor(rmdat.rm$dia.exbf2)
rmdat.rm$diarrhea<-as.factor(rmdat.rm$diarrhea)
rmdat.rm$month.exbf2l<-mapvalues(rmdat.rm$month.exbf2,from=c("<=2 months",">2 months"),to=c("Duration EBF <=2 months","Duration EBF >2 months"))
b2<-gamm(age.predicted~s(age.sample,by=dia.exbf2) +dia.exbf2,family=gaussian,
data=rmdat.rm,random=list(personid=~1))
pred <- predict(b2$gam, newdata = rmdat.rm,se.fit=TRUE)
datfit<-cbind(rmdat.rm, fit=pred$fit,ul=(pred$fit+(1.96*pred$se.fit)),ll=(pred$fit-(1.96*pred$se.fit)))
pd.rm.exbf2<-ggplot()+ geom_point(data = rmdat.rm, aes(x = age.sample, y = age.predicted, group = personid, colour=diarrhea))+
geom_line(data = rmdat.rm, aes(x = age.sample, y = age.predicted, group = personid, colour=diarrhea),size=0.1)+
geom_line(data = datfit,aes(x = age.sample, y = fit, colour=diarrhea),size = 1.5)+
geom_ribbon(data = datfit,aes(x=age.sample, ymax=ul, ymin=ll,group=diarrhea, fill=diarrhea), alpha=.4)+guides(fill=FALSE)+
scale_x_continuous(breaks=seq(from=0,to=24,by=3),
labels=seq(from=0,to=24,by=3))+
theme(legend.position = c(0.15,0.95),legend.title=element_text(size=8),legend.text=element_text(size=8))+
theme(legend.key.size = unit(0.3, "cm"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
strip.background =element_rect(fill="white"))+
xlab("Chronological age (months)") +ylab("Microbiota age (months)") +
facet_wrap(~month.exbf2l, ncol = 1)
pd.rm.exbf2
GAM part
kable(summary(b2$gam)$p.table)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 10.3788398 | 0.2835665 | 36.6010753 | 0.0000000 |
| dia.exbf2No.>2 months | -1.1507899 | 0.4879123 | -2.3585999 | 0.0186104 |
| dia.exbf2Yes.<=2 months | -1.1945958 | 0.4839537 | -2.4684094 | 0.0138028 |
| dia.exbf2Yes.>2 months | -0.5097696 | 0.8117265 | -0.6280066 | 0.5301988 |
kable(summary(b2$gam)$s.table)
| edf | Ref.df | F | p-value | |
|---|---|---|---|---|
| s(age.sample):dia.exbf2No.<=2 months | 5.032806 | 5.032806 | 251.05395 | 0 |
| s(age.sample):dia.exbf2No.>2 months | 4.191756 | 4.191756 | 149.07931 | 0 |
| s(age.sample):dia.exbf2Yes.<=2 months | 1.000000 | 1.000000 | 37.58735 | 0 |
| s(age.sample):dia.exbf2Yes.>2 months | 1.000000 | 1.000000 | 49.79342 | 0 |
LME part
kable(summary(b2$lme)$tTable)
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| X(Intercept) | 10.3788398 | 0.2843456 | 689 | 36.500798 | 0.0000000 |
| Xdia.exbf2No.>2 months | -1.1507899 | 0.4892527 | 689 | -2.352138 | 0.0189463 |
| Xdia.exbf2Yes.<=2 months | -1.1945958 | 0.4852832 | 689 | -2.461647 | 0.0140740 |
| Xdia.exbf2Yes.>2 months | -0.5097696 | 0.8139565 | 689 | -0.626286 | 0.5313348 |
| Xs(age.sample):dia.exbf2No.<=2 monthsFx1 | 1.8308388 | 1.3486999 | 689 | 1.357484 | 0.1750718 |
| Xs(age.sample):dia.exbf2No.>2 monthsFx1 | 1.6657329 | 1.3242074 | 689 | 1.257909 | 0.2088507 |
| Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 | 3.4720580 | 0.5678815 | 689 | 6.114054 | 0.0000000 |
| Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 | 5.0913209 | 0.7234958 | 689 | 7.037112 | 0.0000000 |
Test for heterogeneity (interaction)
fit<-glmer(age.predicted~age.sample +month.exbf2*diarrhea+(1|personid),data=rmdat.rm)
tab<-summary(fit)$coefficients
tabz<-tab[,"Estimate"]/tab[,"Std. Error"]
tabp<-2*pnorm(-abs(tabz))
tab<-cbind(tab,p.val=tabp)
kable(tab)
| Estimate | Std. Error | t value | p.val | |
|---|---|---|---|---|
| (Intercept) | 3.7678265 | 0.3316981 | 11.359203 | 0.0000000 |
| age.sample | 0.6832422 | 0.0173814 | 39.308910 | 0.0000000 |
| month.exbf2>2 months | -1.2353814 | 0.5064509 | -2.439291 | 0.0147161 |
| diarrheaYes | -1.0072569 | 0.5024243 | -2.004793 | 0.0449851 |
| month.exbf2>2 months:diarrheaYes | 1.8143024 | 0.9006571 | 2.014421 | 0.0439654 |
With GAMM fit and 95%CI.
#use non-standardized alpha
load(paste(dir,"data/alphamean7.pooled.rda",sep=""))
alpha.m.rm<-alpha.m.rm %>% group_by(personid) %>% arrange(personid,age.sample) %>%
mutate(month.food6=cut(month.food, breaks=c(-Inf, 6, Inf), labels=c("<=6 months",">6 months")),
month.food5=cut(month.food, breaks=c(-Inf, 5, Inf), labels=c("<=5 months",">5 months")),
month.food4=cut(month.food, breaks=c(-Inf, 4, Inf), labels=c("<=4 months",">4 months")),
month.foodr=as.factor(sort(round(month.food,0))),
month.exbf3=cut(month.exbf, breaks=c(-Inf, 3, Inf), labels=c("<=3 months",">3 months")),
month.exbf2=cut(month.exbf, breaks=c(-Inf, 2, Inf), labels=c("<=2 months",">2 months")),
month.exbf1=cut(month.exbf, breaks=c(-Inf, 1, Inf), labels=c("<=1 months",">1 months")),
month.exbfr=as.factor(sort(round(month.exbf,0))))
alpha.m.rm$dia.exbf2<-paste(alpha.m.rm$diarrhea,alpha.m.rm$month.exbf2,sep=".")
alpha.m.rm<-as.data.frame(alpha.m.rm)
alpha.m.rm$dia.exbf2<-as.factor(alpha.m.rm$dia.exbf2)
alpha.m.rm$diarrhea<-as.factor(alpha.m.rm$diarrhea)
alpha.m.rm$month.exbf2l<-mapvalues(alpha.m.rm$month.exbf2,from=c("<=2 months",">2 months"),to=c("Duration EBF <=2 months","Duration EBF >2 months"))
b2<-gamm(shannon~s(age.sample,by=dia.exbf2) +dia.exbf2,family=gaussian,
data=alpha.m.rm,random=list(personid=~1))
pred <- predict(b2$gam, newdata = alpha.m.rm,se.fit=TRUE)
kable(summary(b2$gam)$p.table)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 3.0665206 | 0.0823202 | 37.2511259 | 0.0000000 |
| dia.exbf2No.>2 months | -0.1567257 | 0.1292484 | -1.2125925 | 0.2255782 |
| dia.exbf2Yes.<=2 months | -0.5837970 | 0.1257959 | -4.6408273 | 0.0000039 |
| dia.exbf2Yes.>2 months | -0.0919724 | 0.1901039 | -0.4838004 | 0.6286357 |
kable(summary(b2$gam)$s.table)
| edf | Ref.df | F | p-value | |
|---|---|---|---|---|
| s(age.sample):dia.exbf2No.<=2 months | 1.000000 | 1.000000 | 511.09021 | 0.00e+00 |
| s(age.sample):dia.exbf2No.>2 months | 5.974811 | 5.974811 | 75.05942 | 0.00e+00 |
| s(age.sample):dia.exbf2Yes.<=2 months | 1.000000 | 1.000000 | 15.96302 | 6.93e-05 |
| s(age.sample):dia.exbf2Yes.>2 months | 1.000000 | 1.000000 | 43.95975 | 0.00e+00 |
kable(summary(b2$lme)$tTable)
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| X(Intercept) | 3.0665206 | 0.0824874 | 935 | 37.175641 | 0.0000000 |
| Xdia.exbf2No.>2 months | -0.1567257 | 0.1295109 | 935 | -1.210135 | 0.2265328 |
| Xdia.exbf2Yes.<=2 months | -0.5837970 | 0.1260513 | 935 | -4.631425 | 0.0000041 |
| Xdia.exbf2Yes.>2 months | -0.0919724 | 0.1904900 | 935 | -0.482820 | 0.6293366 |
| Xs(age.sample):dia.exbf2No.<=2 monthsFx1 | 0.8428954 | 0.0373599 | 935 | 22.561490 | 0.0000000 |
| Xs(age.sample):dia.exbf2No.>2 monthsFx1 | -0.7155803 | 0.5005884 | 935 | -1.429479 | 0.1532008 |
| Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 | 0.5554706 | 0.1393107 | 935 | 3.987278 | 0.0000720 |
| Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 | 0.9760833 | 0.1475164 | 935 | 6.616780 | 0.0000000 |
datfit<-cbind(alpha.m.rm, fit=pred$fit,ul=(pred$fit+(1.96*pred$se.fit)),ll=(pred$fit-(1.96*pred$se.fit)))
pd.s.exbf2<-ggplot()+ geom_point(data = alpha.m.rm, aes(x = age.sample, y = shannon, group = personid, colour=diarrhea))+
geom_line(data = alpha.m.rm, aes(x = age.sample, y = shannon, group = personid, colour=diarrhea),size=0.1)+
geom_line(data = datfit,aes(x = age.sample, y = fit, colour=diarrhea),size = 1.5)+
geom_ribbon(data = datfit,aes(x=age.sample, ymax=ul, ymin=ll,group=diarrhea, fill=diarrhea), alpha=.4)+guides(fill=FALSE)+
theme(legend.position = c(0.15,0.95),legend.title=element_text(size=8),legend.text=element_text(size=8))+
theme(legend.key.size = unit(0.3, "cm"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
strip.background =element_rect(fill="white"))+
scale_x_continuous(breaks=seq(from=0,to=24,by=3),
labels=seq(from=0,to=24,by=3))+
xlab("Chronological age (months)") +ylab("Shannon index") +
facet_wrap(~month.exbf2l, ncol = 1)
pd.s.exbf2
#combine 4 plots
grid.arrange(pd.rm.exbf2,pd.s.exbf2,p.dia.exbf2.6plus.l5noleg$p,p.dia.bf.6plus.l5.nolegend$p,nrow=1)
GAM part
kable(summary(b2$gam)$p.table)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 3.0665206 | 0.0823202 | 37.2511259 | 0.0000000 |
| dia.exbf2No.>2 months | -0.1567257 | 0.1292484 | -1.2125925 | 0.2255782 |
| dia.exbf2Yes.<=2 months | -0.5837970 | 0.1257959 | -4.6408273 | 0.0000039 |
| dia.exbf2Yes.>2 months | -0.0919724 | 0.1901039 | -0.4838004 | 0.6286357 |
kable(summary(b2$gam)$s.table)
| edf | Ref.df | F | p-value | |
|---|---|---|---|---|
| s(age.sample):dia.exbf2No.<=2 months | 1.000000 | 1.000000 | 511.09021 | 0.00e+00 |
| s(age.sample):dia.exbf2No.>2 months | 5.974811 | 5.974811 | 75.05942 | 0.00e+00 |
| s(age.sample):dia.exbf2Yes.<=2 months | 1.000000 | 1.000000 | 15.96302 | 6.93e-05 |
| s(age.sample):dia.exbf2Yes.>2 months | 1.000000 | 1.000000 | 43.95975 | 0.00e+00 |
LME part
kable(summary(b2$lme)$tTable)
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| X(Intercept) | 3.0665206 | 0.0824874 | 935 | 37.175641 | 0.0000000 |
| Xdia.exbf2No.>2 months | -0.1567257 | 0.1295109 | 935 | -1.210135 | 0.2265328 |
| Xdia.exbf2Yes.<=2 months | -0.5837970 | 0.1260513 | 935 | -4.631425 | 0.0000041 |
| Xdia.exbf2Yes.>2 months | -0.0919724 | 0.1904900 | 935 | -0.482820 | 0.6293366 |
| Xs(age.sample):dia.exbf2No.<=2 monthsFx1 | 0.8428954 | 0.0373599 | 935 | 22.561490 | 0.0000000 |
| Xs(age.sample):dia.exbf2No.>2 monthsFx1 | -0.7155803 | 0.5005884 | 935 | -1.429479 | 0.1532008 |
| Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 | 0.5554706 | 0.1393107 | 935 | 3.987278 | 0.0000720 |
| Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 | 0.9760833 | 0.1475164 | 935 | 6.616780 | 0.0000000 |
Test for heterogeneity (interaction)
fit<-glmer(shannon~age.sample +month.exbf2*diarrhea+(1|personid),data=alpha.m.rm)
tab<-summary(fit)$coefficients
tabz<-tab[,"Estimate"]/tab[,"Std. Error"]
tabp<-2*pnorm(-abs(tabz))
tab<-cbind(tab,p.val=tabp)
kable(tab)
| Estimate | Std. Error | t value | p.val | |
|---|---|---|---|---|
| (Intercept) | 1.8029966 | 0.0917394 | 19.653450 | 0.0000000 |
| age.sample | 0.1228124 | 0.0040692 | 30.181133 | 0.0000000 |
| month.exbf2>2 months | -0.1604039 | 0.1316082 | -1.218798 | 0.2229207 |
| diarrheaYes | -0.5210706 | 0.1247534 | -4.176806 | 0.0000296 |
| month.exbf2>2 months:diarrheaYes | 0.5706337 | 0.1973949 | 2.890823 | 0.0038423 |
b2<-gamm(observed_species~s(age.sample,by=dia.exbf2) +dia.exbf2,family=gaussian,
data=alpha.m.rm,random=list(personid=~1))
pred <- predict(b2$gam, newdata = alpha.m.rm,se.fit=TRUE)
kable(summary(b2$gam)$p.table)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 151.196675 | 8.778134 | 17.2242389 | 0.0000000 |
| dia.exbf2No.>2 months | 6.376610 | 13.831570 | 0.4610185 | 0.6448877 |
| dia.exbf2Yes.<=2 months | -32.741233 | 8.978024 | -3.6468195 | 0.0002795 |
| dia.exbf2Yes.>2 months | 8.288239 | 17.024836 | 0.4868322 | 0.6264862 |
kable(summary(b2$gam)$s.table)
| edf | Ref.df | F | p-value | |
|---|---|---|---|---|
| s(age.sample):dia.exbf2No.<=2 months | 2.241236 | 2.241236 | 186.022933 | 0.0000000 |
| s(age.sample):dia.exbf2No.>2 months | 4.803475 | 4.803475 | 86.288380 | 0.0000000 |
| s(age.sample):dia.exbf2Yes.<=2 months | 1.000000 | 1.000000 | 9.841034 | 0.0017564 |
| s(age.sample):dia.exbf2Yes.>2 months | 1.000000 | 1.000000 | 34.546738 | 0.0000000 |
kable(summary(b2$lme)$tTable)
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| X(Intercept) | 151.196675 | 8.795958 | 935 | 17.1893366 | 0.0000000 |
| Xdia.exbf2No.>2 months | 6.376610 | 13.859654 | 935 | 0.4600843 | 0.6455627 |
| Xdia.exbf2Yes.<=2 months | -32.741233 | 8.996254 | 935 | -3.6394298 | 0.0002882 |
| Xdia.exbf2Yes.>2 months | 8.288239 | 17.059405 | 935 | 0.4858457 | 0.6271904 |
| Xs(age.sample):dia.exbf2No.<=2 monthsFx1 | 51.801469 | 8.730802 | 935 | 5.9331857 | 0.0000000 |
| Xs(age.sample):dia.exbf2No.>2 monthsFx1 | -19.877541 | 26.849174 | 935 | -0.7403409 | 0.4592790 |
| Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 | 31.121465 | 9.940785 | 935 | 3.1306850 | 0.0017981 |
| Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 | 61.612841 | 10.503858 | 935 | 5.8657345 | 0.0000000 |
datfit<-cbind(alpha.m.rm, fit=pred$fit,ul=(pred$fit+(1.96*pred$se.fit)),ll=(pred$fit-(1.96*pred$se.fit)))
pd.os<-ggplot()+ geom_point(data = alpha.m.rm, aes(x = age.sample, y = observed_species, group = personid, colour=diarrhea))+
geom_line(data = alpha.m.rm, aes(x = age.sample, y = observed_species, group = personid, colour=diarrhea))+
geom_line(data = datfit,aes(x = age.sample, y = fit, colour=diarrhea),size = 1.5)+
geom_ribbon(data = datfit,aes(x=age.sample, ymax=ul, ymin=ll,group=diarrhea), alpha=.5)+
theme(legend.key.size = unit(0.5, "cm"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())+ # strip.background = element_blank() element_rect(fill="white")
scale_x_continuous(breaks=seq(from=0,to=24,by=3),
labels=seq(from=0,to=24,by=3))+
theme(legend.position = "none")+
xlab("Chronological age") +ylab("Observed_species") +facet_grid(.~month.exbf2)
pd.os
GAM part
kable(summary(b2$gam)$p.table)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 151.196675 | 8.778134 | 17.2242389 | 0.0000000 |
| dia.exbf2No.>2 months | 6.376610 | 13.831570 | 0.4610185 | 0.6448877 |
| dia.exbf2Yes.<=2 months | -32.741233 | 8.978024 | -3.6468195 | 0.0002795 |
| dia.exbf2Yes.>2 months | 8.288239 | 17.024836 | 0.4868322 | 0.6264862 |
kable(summary(b2$gam)$s.table)
| edf | Ref.df | F | p-value | |
|---|---|---|---|---|
| s(age.sample):dia.exbf2No.<=2 months | 2.241236 | 2.241236 | 186.022933 | 0.0000000 |
| s(age.sample):dia.exbf2No.>2 months | 4.803475 | 4.803475 | 86.288380 | 0.0000000 |
| s(age.sample):dia.exbf2Yes.<=2 months | 1.000000 | 1.000000 | 9.841034 | 0.0017564 |
| s(age.sample):dia.exbf2Yes.>2 months | 1.000000 | 1.000000 | 34.546738 | 0.0000000 |
LME part
kable(summary(b2$lme)$tTable)
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| X(Intercept) | 151.196675 | 8.795958 | 935 | 17.1893366 | 0.0000000 |
| Xdia.exbf2No.>2 months | 6.376610 | 13.859654 | 935 | 0.4600843 | 0.6455627 |
| Xdia.exbf2Yes.<=2 months | -32.741233 | 8.996254 | 935 | -3.6394298 | 0.0002882 |
| Xdia.exbf2Yes.>2 months | 8.288239 | 17.059405 | 935 | 0.4858457 | 0.6271904 |
| Xs(age.sample):dia.exbf2No.<=2 monthsFx1 | 51.801469 | 8.730802 | 935 | 5.9331857 | 0.0000000 |
| Xs(age.sample):dia.exbf2No.>2 monthsFx1 | -19.877541 | 26.849174 | 935 | -0.7403409 | 0.4592790 |
| Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 | 31.121465 | 9.940785 | 935 | 3.1306850 | 0.0017981 |
| Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 | 61.612841 | 10.503858 | 935 | 5.8657345 | 0.0000000 |
Test for heterogeneity (interaction)
fit<-glmer(observed_species~age.sample +month.exbf2*diarrhea+(1|personid),data=alpha.m.rm)
tab<-summary(fit)$coefficients
tabz<-tab[,"Estimate"]/tab[,"Std. Error"]
tabp<-2*pnorm(-abs(tabz))
tab<-cbind(tab,p.val=tabp)
kable(tab)
| Estimate | Std. Error | t value | p.val | |
|---|---|---|---|---|
| (Intercept) | 67.218370 | 9.3632479 | 7.1789587 | 0.0000000 |
| age.sample | 8.165080 | 0.2895546 | 28.1987576 | 0.0000000 |
| month.exbf2>2 months | 6.548791 | 14.1631117 | 0.4623836 | 0.6438062 |
| diarrheaYes | -27.156839 | 8.8730060 | -3.0606132 | 0.0022088 |
| month.exbf2>2 months:diarrheaYes | 28.115237 | 14.0092249 | 2.0069088 | 0.0447594 |
b2<-gamm(pd_whole_tree~s(age.sample,by=dia.exbf2) +dia.exbf2,family=gaussian,
data=alpha.m.rm,random=list(personid=~1))
pred <- predict(b2$gam, newdata = alpha.m.rm,se.fit=TRUE)
kable(summary(b2$gam)$p.table)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 11.1473183 | 0.4242502 | 26.2753392 | 0.0000000 |
| dia.exbf2No.>2 months | 0.2157323 | 0.6676390 | 0.3231271 | 0.7466679 |
| dia.exbf2Yes.<=2 months | -2.4295713 | 0.5177632 | -4.6924371 | 0.0000031 |
| dia.exbf2Yes.>2 months | -0.1330212 | 0.8797770 | -0.1511987 | 0.8798500 |
kable(summary(b2$gam)$s.table)
| edf | Ref.df | F | p-value | |
|---|---|---|---|---|
| s(age.sample):dia.exbf2No.<=2 months | 1.00000 | 1.00000 | 651.06494 | 0.0e+00 |
| s(age.sample):dia.exbf2No.>2 months | 4.26171 | 4.26171 | 146.66378 | 0.0e+00 |
| s(age.sample):dia.exbf2Yes.<=2 months | 1.00000 | 1.00000 | 20.80080 | 5.7e-06 |
| s(age.sample):dia.exbf2Yes.>2 months | 1.00000 | 1.00000 | 48.09244 | 0.0e+00 |
kable(summary(b2$lme)$tTable)
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| X(Intercept) | 11.1473183 | 0.4251117 | 935 | 26.2220945 | 0.0000000 |
| Xdia.exbf2No.>2 months | 0.2157323 | 0.6689947 | 935 | 0.3224723 | 0.7471670 |
| Xdia.exbf2Yes.<=2 months | -2.4295713 | 0.5188143 | 935 | -4.6829302 | 0.0000032 |
| Xdia.exbf2Yes.>2 months | -0.1330212 | 0.8815636 | 935 | -0.1508923 | 0.8800932 |
| Xs(age.sample):dia.exbf2No.<=2 monthsFx1 | 3.9257946 | 0.1541688 | 935 | 25.4642659 | 0.0000000 |
| Xs(age.sample):dia.exbf2No.>2 monthsFx1 | 0.5241955 | 1.3248785 | 935 | 0.3956555 | 0.6924494 |
| Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 | 2.6094784 | 0.5733168 | 935 | 4.5515473 | 0.0000060 |
| Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 | 4.1958246 | 0.6062613 | 935 | 6.9208183 | 0.0000000 |
datfit<-cbind(alpha.m.rm, fit=pred$fit,ul=(pred$fit+(1.96*pred$se.fit)),ll=(pred$fit-(1.96*pred$se.fit)))
pd.wt<-ggplot()+ geom_point(data = alpha.m.rm, aes(x = age.sample, y = pd_whole_tree, group = personid, colour=diarrhea))+
geom_line(data = alpha.m.rm, aes(x = age.sample, y = pd_whole_tree, group = personid, colour=diarrhea))+
geom_line(data = datfit,aes(x = age.sample, y = fit, colour=diarrhea),size = 1.5)+
geom_ribbon(data = datfit,aes(x=age.sample, ymax=ul, ymin=ll,group=diarrhea), alpha=.5)+
theme(legend.key.size = unit(0.5, "cm"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())+ # strip.background = element_blank() element_rect(fill="white")
scale_x_continuous(breaks=seq(from=0,to=24,by=3),
labels=seq(from=0,to=24,by=3))+
theme(legend.position = "none")+
xlab("Chronological age") +ylab("Pd_whole_tree") +facet_grid(.~month.exbf2)
pd.wt
GAM part
kable(summary(b2$gam)$p.table)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 11.1473183 | 0.4242502 | 26.2753392 | 0.0000000 |
| dia.exbf2No.>2 months | 0.2157323 | 0.6676390 | 0.3231271 | 0.7466679 |
| dia.exbf2Yes.<=2 months | -2.4295713 | 0.5177632 | -4.6924371 | 0.0000031 |
| dia.exbf2Yes.>2 months | -0.1330212 | 0.8797770 | -0.1511987 | 0.8798500 |
kable(summary(b2$gam)$s.table)
| edf | Ref.df | F | p-value | |
|---|---|---|---|---|
| s(age.sample):dia.exbf2No.<=2 months | 1.00000 | 1.00000 | 651.06494 | 0.0e+00 |
| s(age.sample):dia.exbf2No.>2 months | 4.26171 | 4.26171 | 146.66378 | 0.0e+00 |
| s(age.sample):dia.exbf2Yes.<=2 months | 1.00000 | 1.00000 | 20.80080 | 5.7e-06 |
| s(age.sample):dia.exbf2Yes.>2 months | 1.00000 | 1.00000 | 48.09244 | 0.0e+00 |
LME part
kable(summary(b2$lme)$tTable)
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| X(Intercept) | 11.1473183 | 0.4251117 | 935 | 26.2220945 | 0.0000000 |
| Xdia.exbf2No.>2 months | 0.2157323 | 0.6689947 | 935 | 0.3224723 | 0.7471670 |
| Xdia.exbf2Yes.<=2 months | -2.4295713 | 0.5188143 | 935 | -4.6829302 | 0.0000032 |
| Xdia.exbf2Yes.>2 months | -0.1330212 | 0.8815636 | 935 | -0.1508923 | 0.8800932 |
| Xs(age.sample):dia.exbf2No.<=2 monthsFx1 | 3.9257946 | 0.1541688 | 935 | 25.4642659 | 0.0000000 |
| Xs(age.sample):dia.exbf2No.>2 monthsFx1 | 0.5241955 | 1.3248785 | 935 | 0.3956555 | 0.6924494 |
| Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 | 2.6094784 | 0.5733168 | 935 | 4.5515473 | 0.0000060 |
| Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 | 4.1958246 | 0.6062613 | 935 | 6.9208183 | 0.0000000 |
Test for heterogeneity (interaction)
fit<-glmer(pd_whole_tree~age.sample +month.exbf2*diarrhea+(1|personid),data=alpha.m.rm)
tab<-summary(fit)$coefficients
tabz<-tab[,"Estimate"]/tab[,"Std. Error"]
tabp<-2*pnorm(-abs(tabz))
tab<-cbind(tab,p.val=tabp)
kable(tab)
| Estimate | Std. Error | t value | p.val | |
|---|---|---|---|---|
| (Intercept) | 5.1010594 | 0.4591138 | 11.1106653 | 0.0000000 |
| age.sample | 0.5894225 | 0.0166254 | 35.4531752 | 0.0000000 |
| month.exbf2>2 months | 0.2033228 | 0.6822247 | 0.2980291 | 0.7656809 |
| diarrheaYes | -2.1261538 | 0.5095509 | -4.1726032 | 0.0000301 |
| month.exbf2>2 months:diarrheaYes | 1.7538795 | 0.8050786 | 2.1785195 | 0.0293674 |
ggplot(data=alpha.m.rm,aes(x=age.sample, y=pd_whole_tree, colour=diarrhea, group=personid)) +geom_point()+geom_line() +
geom_smooth(data=alpha.m.rm,aes(x=age.sample, y=pd_whole_tree,group=diarrhea))+facet_grid(.~month.exbf2)
b2<-gamm(chao1~s(age.sample,by=dia.exbf2) +dia.exbf2,family=gaussian,
data=alpha.m.rm,random=list(personid=~1))
pred <- predict(b2$gam, newdata = alpha.m.rm,se.fit=TRUE)
kable(summary(b2$gam)$p.table)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 367.19115 | 24.93875 | 14.7237215 | 0.0000000 |
| dia.exbf2No.>2 months | 16.36035 | 39.31689 | 0.4161151 | 0.6774169 |
| dia.exbf2Yes.<=2 months | -86.62794 | 23.43335 | -3.6967801 | 0.0002305 |
| dia.exbf2Yes.>2 months | 28.78746 | 47.08230 | 0.6114285 | 0.5410577 |
kable(summary(b2$gam)$s.table)
| edf | Ref.df | F | p-value | |
|---|---|---|---|---|
| s(age.sample):dia.exbf2No.<=2 months | 1.502023 | 1.502023 | 261.413376 | 0.0000000 |
| s(age.sample):dia.exbf2No.>2 months | 4.677640 | 4.677640 | 76.504447 | 0.0000000 |
| s(age.sample):dia.exbf2Yes.<=2 months | 1.000000 | 1.000000 | 7.810505 | 0.0052936 |
| s(age.sample):dia.exbf2Yes.>2 months | 1.000000 | 1.000000 | 29.909461 | 0.0000001 |
kable(summary(b2$lme)$tTable)
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| X(Intercept) | 367.19115 | 24.98938 | 935 | 14.6938872 | 0.0000000 |
| Xdia.exbf2No.>2 months | 16.36035 | 39.39672 | 935 | 0.4152720 | 0.6780381 |
| Xdia.exbf2Yes.<=2 months | -86.62794 | 23.48093 | 935 | -3.6892894 | 0.0002379 |
| Xdia.exbf2Yes.>2 months | 28.78746 | 47.17789 | 935 | 0.6101897 | 0.5418844 |
| Xs(age.sample):dia.exbf2No.<=2 monthsFx1 | 134.05173 | 12.55943 | 935 | 10.6733923 | 0.0000000 |
| Xs(age.sample):dia.exbf2No.>2 monthsFx1 | -43.96611 | 67.65832 | 935 | -0.6498257 | 0.5159644 |
| Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 | 72.36395 | 25.94559 | 935 | 2.7890649 | 0.0053934 |
| Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 | 149.59600 | 27.40922 | 935 | 5.4578722 | 0.0000001 |
datfit<-cbind(alpha.m.rm, fit=pred$fit,ul=(pred$fit+(1.96*pred$se.fit)),ll=(pred$fit-(1.96*pred$se.fit)))
pd.chao<-ggplot()+ geom_point(data = alpha.m.rm, aes(x = age.sample, y = chao1, group = personid, colour=diarrhea))+
geom_line(data = alpha.m.rm, aes(x = age.sample, y = chao1, group = personid, colour=diarrhea))+
geom_line(data = datfit,aes(x = age.sample, y = fit, colour=diarrhea),size = 1.5)+
geom_ribbon(data = datfit,aes(x=age.sample, ymax=ul, ymin=ll,group=diarrhea), alpha=.5)+
theme(legend.key.size = unit(0.5, "cm"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())+ # strip.background = element_blank() element_rect(fill="white")
scale_x_continuous(breaks=seq(from=0,to=24,by=3),
labels=seq(from=0,to=24,by=3))+
theme(legend.position = "bottom")+
xlab("Chronological age") +ylab("Chao1") +facet_grid(.~month.exbf2)
pd.chao
GAM part
kable(summary(b2$gam)$p.table)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 367.19115 | 24.93875 | 14.7237215 | 0.0000000 |
| dia.exbf2No.>2 months | 16.36035 | 39.31689 | 0.4161151 | 0.6774169 |
| dia.exbf2Yes.<=2 months | -86.62794 | 23.43335 | -3.6967801 | 0.0002305 |
| dia.exbf2Yes.>2 months | 28.78746 | 47.08230 | 0.6114285 | 0.5410577 |
kable(summary(b2$gam)$s.table)
| edf | Ref.df | F | p-value | |
|---|---|---|---|---|
| s(age.sample):dia.exbf2No.<=2 months | 1.502023 | 1.502023 | 261.413376 | 0.0000000 |
| s(age.sample):dia.exbf2No.>2 months | 4.677640 | 4.677640 | 76.504447 | 0.0000000 |
| s(age.sample):dia.exbf2Yes.<=2 months | 1.000000 | 1.000000 | 7.810505 | 0.0052936 |
| s(age.sample):dia.exbf2Yes.>2 months | 1.000000 | 1.000000 | 29.909461 | 0.0000001 |
LME part
kable(summary(b2$lme)$tTable)
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| X(Intercept) | 367.19115 | 24.98938 | 935 | 14.6938872 | 0.0000000 |
| Xdia.exbf2No.>2 months | 16.36035 | 39.39672 | 935 | 0.4152720 | 0.6780381 |
| Xdia.exbf2Yes.<=2 months | -86.62794 | 23.48093 | 935 | -3.6892894 | 0.0002379 |
| Xdia.exbf2Yes.>2 months | 28.78746 | 47.17789 | 935 | 0.6101897 | 0.5418844 |
| Xs(age.sample):dia.exbf2No.<=2 monthsFx1 | 134.05173 | 12.55943 | 935 | 10.6733923 | 0.0000000 |
| Xs(age.sample):dia.exbf2No.>2 monthsFx1 | -43.96611 | 67.65832 | 935 | -0.6498257 | 0.5159644 |
| Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 | 72.36395 | 25.94559 | 935 | 2.7890649 | 0.0053934 |
| Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 | 149.59600 | 27.40922 | 935 | 5.4578722 | 0.0000001 |
Test for heterogeneity (interaction)
fit<-glmer(chao1~age.sample +month.exbf2*diarrhea+(1|personid),data=alpha.m.rm)
tab<-summary(fit)$coefficients
tabz<-tab[,"Estimate"]/tab[,"Std. Error"]
tabp<-2*pnorm(-abs(tabz))
tab<-cbind(tab,p.val=tabp)
kable(tab)
| Estimate | Std. Error | t value | p.val | |
|---|---|---|---|---|
| (Intercept) | 161.48642 | 26.4705453 | 6.1006078 | 0.0000000 |
| age.sample | 20.00874 | 0.7538543 | 26.5419213 | 0.0000000 |
| month.exbf2>2 months | 16.28471 | 40.3235504 | 0.4038512 | 0.6863222 |
| diarrheaYes | -72.27871 | 23.0991493 | -3.1290636 | 0.0017536 |
| month.exbf2>2 months:diarrheaYes | 83.14805 | 36.4602574 | 2.2805118 | 0.0225774 |
grid.arrange(pd.os,pd.wt,pd.chao,nrow=3)
sessionInfo()
R version 3.4.2 (2017-09-28)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gplots_3.0.1 bindrcpp_0.2 meta_4.9-0
[4] randomForest_4.6-12 gdata_2.18.0 scales_0.5.0
[7] RColorBrewer_1.1-2 geepack_1.2-1 zoo_1.8-0
[10] itsadug_2.3 plotfunctions_1.3 mgcv_1.8-22
[13] nlme_3.1-131 reshape2_1.4.3 lmerTest_2.0-36
[16] sjPlot_2.4.0 sjmisc_2.6.3 lme4_1.1-15
[19] Matrix_1.2-12 tidyr_0.7.2 dtplyr_0.0.2
[22] data.table_1.10.4-3 dplyr_0.7.4 date_1.2-37
[25] lubridate_1.7.1 chron_2.3-51 gmodels_2.16.2
[28] gridExtra_2.3 plyr_1.8.4 digest_0.6.12
[31] caret_6.0-78 ggplot2_2.2.1 lattice_0.20-35
[34] knitr_1.17
loaded via a namespace (and not attached):
[1] backports_1.1.1 Hmisc_4.0-3 blme_1.0-4
[4] lazyeval_0.2.1 TMB_1.7.11 splines_3.4.2
[7] TH.data_1.0-8 foreach_1.4.3 htmltools_0.3.6
[10] magrittr_1.5 checkmate_1.8.5 cluster_2.0.6
[13] sfsmisc_1.1-1 recipes_0.1.1 modelr_0.1.1
[16] gower_0.1.2 dimRed_0.1.0 sandwich_2.4-0
[19] colorspace_1.3-2 haven_1.1.0 crayon_1.3.4
[22] bindr_0.1 survival_2.41-3 iterators_1.0.8
[25] glue_1.2.0 DRR_0.0.2 gtable_0.2.0
[28] ipred_0.9-6 sjstats_0.13.0 kernlab_0.9-25
[31] ddalpha_1.3.1 DEoptimR_1.0-8 abind_1.4-5
[34] mvtnorm_1.0-6 ggeffects_0.3.0 Rcpp_0.12.14
[37] xtable_1.8-2 merTools_0.3.0 htmlTable_1.11.0
[40] foreign_0.8-69 Formula_1.2-2 stats4_3.4.2
[43] prediction_0.2.0 lava_1.5.1 survey_3.32-1
[46] prodlim_1.6.1 DT_0.2 htmlwidgets_0.9
[49] acepack_1.4.1 modeltools_0.2-21 pkgconfig_2.0.1
[52] nnet_7.3-12 labeling_0.3 tidyselect_0.2.3
[55] rlang_0.1.4 munsell_0.4.3 tools_3.4.2
[58] cli_1.0.0 sjlabelled_1.0.5 broom_0.4.3
[61] evaluate_0.10.1 stringr_1.2.0 arm_1.9-3
[64] yaml_2.1.15 ModelMetrics_1.1.0 robustbase_0.92-8
[67] caTools_1.17.1 purrr_0.2.4 coin_1.2-2
[70] mime_0.5 RcppRoll_0.2.2 compiler_3.4.2
[73] bayesplot_1.4.0 rstudioapi_0.7.0-9000 tibble_1.3.4
[76] stringi_1.1.6 highr_0.6 forcats_0.2.0
[79] psych_1.7.8 nloptr_1.0.4 effects_4.0-0
[82] stringdist_0.9.4.6 pwr_1.2-1 lmtest_0.9-35
[85] bitops_1.0-6 httpuv_1.3.5 R6_2.2.2
[88] latticeExtra_0.6-28 KernSmooth_2.23-15 codetools_0.2-15
[91] MASS_7.3-47 gtools_3.5.0 assertthat_0.2.0
[94] CVST_0.2-1 rprojroot_1.2 withr_2.1.0
[97] mnormt_1.5-5 multcomp_1.4-8 parallel_3.4.2
[100] grid_3.4.2 rpart_4.1-11 timeDate_3042.101
[103] coda_0.19-1 glmmTMB_0.2.0 class_7.3-14
[106] minqa_1.2.4 rmarkdown_1.8 snakecase_0.5.1
[109] carData_3.0-0 shiny_1.0.5 base64enc_0.1-3